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Varma, Ankitha, and Dr Kamalpreet Kaur. "Survey on content based image retrieval." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 471. http://dx.doi.org/10.14419/ijet.v7i4.5.21136.
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Now-a-days, because of the advancement in the digital technology and the use of internet, a huge amount of digital data is available in the form of medical images, remote sensing, digital museums, geographical information, etc. This has lead to the need of accurate and efficient techniques for the search and retrieval of relevant images from such voluminous datasets. Content based image retrieval (CBIR) is one such approach which is increasingly being used to search and retrieve query image from the databases. CBIR combines features of color, texture as well as shape which ease out the process of extracting desired information from the retrieved images. This paper pre- sents a systematic and a detailed review of the CBIR method along with the different databases and evaluation parameters used for the analysis. An attempt has been made to include an exhaustive literature survey of the various CBIR approaches.
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MORE, MAHADEV A. "CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (September 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem25835.
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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user applications. Content-based Image retrieval deals with the extraction of knowledge, image data relationship, or other patternsnot expressly keep within the pictures. It uses ways from computer vision, image processing, image retrieval, data retrieval, machine learning, database and artificial intelligence. Rule retrieval has been applied to large image databases. The proposedsystem gives average accuracy of 90%. Keywords— CBIR, Color feature, Shape feature, Texture feature, Feature extraction, Clustering, Image Retrieval.
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Premkumar, M., and R. Sowmya. "Interactive Content Based Image Retrieval using Multiuser Feedback." JOIV : International Journal on Informatics Visualization 1, no. 4 (December 1, 2017): 165. http://dx.doi.org/10.30630/joiv.1.4.57.
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Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.
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Malik, C. K. Mohammed. "Content based Image Retrieval Using Clustering Method." International Academic Journal of Science and Engineering 6, no. 2 (September 26, 2022): 06–12. http://dx.doi.org/10.9756/iajse/v6i2/1910020.
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Content-based image retrieval (CBIR) is the deployment of computer vision methods to the information retrieval challenge, that is, the subject of seeking out digital images in vast databases. Techniques based on automated feature extraction methods for obtaining similar images from image databases are under the purview of CBIR. Traditional content based image retrieval (CBIR) systems extract a single feature at a time and use it to categorize and group images in response to a query. To bridge the gap between high-level concepts and low-level features, our innovative method integrates many feature extraction algorithms. In color-based retrieval, we use quadratic distance formulas to calculate the HSV affinity matrix for photos in the query and the database. Wavelet decomposition at six stages is used in texture-based retrieval. Finding the similarity measures between the query image and the images in the database is done with the help of the Euclidean distance classifier. The integrated method used to decrease the file sizes of the retrieved photographs keeps the user from having to pay as much attention to the process.
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Belattar, Khadidja, Sihem Mostefai, and Amer Draa. "Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis." Journal of Information Technology Research 10, no. 1 (January 2017): 85–108. http://dx.doi.org/10.4018/jitr.2017010106.
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The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.
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Kumar, Suneel, Manoj Kumar Singh, and Manoj Kumar Mishra. "Improve Content-based Image Retrieval using Deep learning model." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2327/1/012028.
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Abstract The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).
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Srinivasa Reddy, K., R. Anandan, K. Kalaivani, and P. Swaminathan. "A comprehensive survey on content based image retrieval system and its application in medical domain." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 181. http://dx.doi.org/10.14419/ijet.v7i2.31.13436.
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Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.
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Fatima, Shaheen. "Explicit Study on Design and Development of Content-based Image Retrieval in Medical Imaging." Journal of Advanced Research in Electronics Engineering and Technology 08, no. 1&2 (August 23, 2021): 1–5. http://dx.doi.org/10.24321/2456.1428.202101.
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Digital Image Databases and documentation provide lot of research areas. Significant among them is, the Content Based Image Retrieval (CBIR) research area for manipulating large amount of image databases and archives. The development in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. There is a continuous research in the area of CBIR systems typically for medical images, which provides a successive algorithm development for achieving generalized methodologies, which could be widely used. The achievement of such system mainly depends upon the strength, accuracy and speed of the retrieval systems. Content Based Image Retrieval (CBIR) system is valuable in medical systems as it provides retrieval of the images from the large dataset based on similarities. The aim of this paper is to discuss the various techniques, the assumptions and its scope suggested by various researchers and setup a further roadmap of the research in the field of CBIR system for medical image.
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Fatima, Shaheen. "Explicit Study on Design and Development of Content-based Image Retrieval in Medical Imaging." Journal of Advanced Research in Electronics Engineering and Technology 08, no. 1&2 (August 23, 2021): 1–5. http://dx.doi.org/10.24321/2456.1428.202101.
Full textAbstract:
Digital Image Databases and documentation provide lot of research areas. Significant among them is, the Content Based Image Retrieval (CBIR) research area for manipulating large amount of image databases and archives. The development in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. There is a continuous research in the area of CBIR systems typically for medical images, which provides a successive algorithm development for achieving generalized methodologies, which could be widely used. The achievement of such system mainly depends upon the strength, accuracy and speed of the retrieval systems. Content Based Image Retrieval (CBIR) system is valuable in medical systems as it provides retrieval of the images from the large dataset based on similarities. The aim of this paper is to discuss the various techniques, the assumptions and its scope suggested by various researchers and setup a further roadmap of the research in the field of CBIR system for medical image.
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Singh, Vibhav Prakash, Rajeev Srivastava, Yadunath Pathak, Shailendra Tiwari, and Kuldeep Kaur. "Content-based image retrieval based on supervised learning and statistical-based moments." Modern Physics Letters B 33, no. 19 (July 8, 2019): 1950213. http://dx.doi.org/10.1142/s0217984919502130.
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Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.
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Chakraverti, Ashish. "Deep Learning based Smart Image Search Engine." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 1577–85. http://dx.doi.org/10.22214/ijraset.2024.58602.
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Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Texture Algorithm, in CBIR. As the Internet sees an exponential growth of different data types, the importance of CBIR continues to grow. In order to efficiently retrieve images, solely relying on image features while ignoring metadata is exactly what we need. As such, this paper is a reminder of the need for CBIR in this changing world. They showed that CBIR continues to be quite effective in the age of the Internet. Their proposed model for CBIR, which integrates ResNet-50-based feature extraction, a neural network model trained on different image datasets, and clustering techniques to make retrieval fast, provides a significant improvement in accuracy and efficiency for content-dependent image retrieval. This methodology is likely to be very useful as we work with the increasingly huge data of vision and beyond on the Internet. It provides a good basis for an effective image search and retrieval system
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Tarjoman, Mana, Emad Fatemizadeh, and Kambiz Badie. "A Content-Based Approach to Medical Images Retrieval." International Journal of Healthcare Information Systems and Informatics 8, no. 2 (April 2013): 15–27. http://dx.doi.org/10.4018/jhisi.2013040102.
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Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency.
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Bilquees, Samina, Hassan Dawood, Hussain Dawood, Nadeem Majeed, Ali Javed, and Muhammad Tariq Mahmood. "Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval." International Journal of Optics 2021 (July 14, 2021): 1–19. http://dx.doi.org/10.1155/2021/4151482.
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In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.
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Arunlal, S. L., N. Santhi, and K. Ramar. "Design and Implementation of Content-Based Natural Image Retrieval Approach Using Feature Distance." International Journal of Image and Graphics 20, no. 02 (April 2020): 2050014. http://dx.doi.org/10.1142/s021946782050014x.
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Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3% in distance based ANN process.
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LIN, HWEI-JEN, YANG-TA KAO, FU-WEN YANG, and PATRICK S. P. WANG. "CONTENT-BASED IMAGE RETRIEVAL TRAINED BY ADABOOST FOR MOBILE APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 04 (June 2006): 525–41. http://dx.doi.org/10.1142/s021800140600482x.
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This paper proposes a Content-Based Image Retrieval (CBIR) system applicable in mobile devices. Due to the fact that different queries to a content-based image retrieval (CBIR) system emphasize different subsets of a large collection of features, most CBIR systems using only a few features are therefore only suitable for retrieving certain types of images. In this research we combine a wide range of features, including edge information, texture energy, and the HSV color distributions, forming a feature space of up to 1053 dimensions, in which the system can search for features most desired by the user. Through a training process using the AdaBoost algorithm9 our system can efficiently search for important features in a large set of features, as indicated by the user, and effectively retrieve the images according to these features. The characteristics of the system meet the requirements of mobile devices for performing image retrieval. The experimental results show that the performance of the proposed system is sufficiently applicable for mobile devices to retrieve images from a huge database.
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Tarjoman, Mana. "A FUZZY FRAMEWORK FOR CONTENT BASED MAGNETIC RESONANCE IMAGES RETRIEVAL USING SALIENCY MAP." Biomedical Engineering: Applications, Basis and Communications 29, no. 05 (October 2017): 1750033. http://dx.doi.org/10.4015/s1016237217500338.
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Content-based image retrieval (CBIR) has turned into an important research field with the advancement in multimedia and imaging technology. The term CBIR has been widely used to describe the process of retrieving desired images from a large collection on the basis of features such as color, texture and shape that can be automatically extracted from the images themselves. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image’s saliency through ants’ movements on the image. The textural features are then calculated from the saliency map of the images. The image retrieval of the proposed CBIR system is based on textural features and the fuzzy approach using Adaptive neuro-fuzzy inference system (ANFIS). Regarding the various categories of images in a database, we define some membership functions in the ANFIS output in order to determine the membership values of the images related to the existing categories. In online image retrieval, a query image is introduced to the system and the relevant images can be retrieved based on query membership values into different classes including normal or tumoral. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.
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Sandhu, Amanbir, and Aarti Kochhar. "Content Based Image Retrieval using Texture, Color and Shape for Image Analysis." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 149–52. http://dx.doi.org/10.24297/ijct.v3i1c.2768.
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Content- Based Image Retrieval(CBIR) or QBIR is the important field of research..Content Based Image retrieval has gained much popularity in the past Content-based image retrieval (CBIR)[1] system has also helped users to retrieve relevant images based on their contents. It represents low level features like texture ,color and shape .In this paper, we compare the several feature extraction techniques [5]i.e..GLCM ,Histogram and shape properties over color, texture and shape The experiments show the similarity between these features and also that the output obtained using this combination of color, texture and shape is better as obtaining output with a single feature
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N, Parvin, and Kavitha P. "Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (July 1, 2017): 226. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp226-233.
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<p>Content-Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extends from surveillance to remote sensing, medical imaging to weather forecasting, security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques.</p><p> </p>
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Khan, Aamir, and Anand Singh Jalal. "A Visual Saliency-Based Approach for Content-Based Image Retrieval." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 1 (January 2021): 1–15. http://dx.doi.org/10.4018/ijcini.2021010101.
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During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the “semantic gap,” which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.
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Gassner, Mathias, Javier Barranco Garcia, Stephanie Tanadini-Lang, Fabio Bertoldo, Fabienne Fröhlich, Matthias Guckenberger, Silvia Haueis, et al. "Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study." JMIR Dermatology 6 (August 24, 2023): e42129. http://dx.doi.org/10.2196/42129.
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Background Previous research studies have demonstrated that medical content image retrieval can play an important role by assisting dermatologists in skin lesion diagnosis. However, current state-of-the-art approaches have not been adopted in routine consultation, partly due to the lack of interpretability limiting trust by clinical users. Objective This study developed a new image retrieval architecture for polarized or dermoscopic imaging guided by interpretable saliency maps. This approach provides better feature extraction, leading to better quantitative retrieval performance as well as providing interpretability for an eventual real-world implementation. Methods Content-based image retrieval (CBIR) algorithms rely on the comparison of image features embedded by convolutional neural network (CNN) against a labeled data set. Saliency maps are computer vision–interpretable methods that highlight the most relevant regions for the prediction made by a neural network. By introducing a fine-tuning stage that includes saliency maps to guide feature extraction, the accuracy of image retrieval is optimized. We refer to this approach as saliency-enhanced CBIR (SE-CBIR). A reader study was designed at the University Hospital Zurich Dermatology Clinic to evaluate SE-CBIR’s retrieval accuracy as well as the impact of the participant’s confidence on the diagnosis. Results SE-CBIR improved the retrieval accuracy by 7% (77% vs 84%) when doing single-lesion retrieval against traditional CBIR. The reader study showed an overall increase in classification accuracy of 22% (62% vs 84%) when the participant is provided with SE-CBIR retrieved images. In addition, the overall confidence in the lesion’s diagnosis increased by 24%. Finally, the use of SE-CBIR as a support tool helped the participants reduce the number of nonmelanoma lesions previously diagnosed as melanoma (overdiagnosis) by 53%. Conclusions SE-CBIR presents better retrieval accuracy compared to traditional CBIR CNN-based approaches. Furthermore, we have shown how these support tools can help dermatologists and residents improve diagnosis accuracy and confidence. Additionally, by introducing interpretable methods, we should expect increased acceptance and use of these tools in routine consultation.
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Zhang, Yinghui, Fengyuan Zhang, Yantong Cui, and Ruoci Ning. "CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS." International Journal of Engineering Technologies and Management Research 5, no. 2 (February 8, 2020): 181–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.161.
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Because of the numerous application of Content-based image retrieval (CBIR) system in various areas, it has always remained a topic of keen interest by the researchers. Fetching of the most similar image from the complete repository by comparing it to the input image in the minimum span of time is the main task of the CBIR. The purpose of the CBIR can vary from different types of requirements like a diagnosis of the illness by the physician, crime investigation, product recommendation by the e-commerce companies, etc. In the present work, CBIR is used for finding the similar patients having Breast cancer. Gray-Level Co-Occurrence Matrix along with histogram and correlation coefficient is used for creating CBIR system. Comparing the images of the area of interest of a present patient with the complete series of the image of a past patient can help in early diagnosis of the disease. CBIR is so much effective that even when the symptoms are not shown by the body the disease can be diagnosed from the sample images.
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Fatima, Shaheen. "A Review on Procedures on Design and Development of Framework for Content Based Image Retrieval." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 438–40. http://dx.doi.org/10.22214/ijraset.2022.40290.
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Abstract: Digital Image Databases and documentation provide lot of research areas. Significant among them is, The Content Based Image Retrieval (CBIR) research area for manipulating large amount of image databases and archives. The development in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. There is a continuous research in the area of CBIR systems typically for medical images, which provides a successive algorithm development for achieving generalized methodologies, which could be widely used. The achievement of such system mainly depends upon the strength, accuracy and speed of the retrieval systems. Content based image retrieval (CBIR) system is valuable in medical systems as it provides retrieval of the images from the large dataset based on similarities. The aim of this paper is to discuss the various techniques, the assumptions and its scope suggested by various researchers and setup a further roadmap of the research in the field of CBIR system for medical image. Keywords: Content–based image retrieval (CBIR), Digital Images, Medical Imaging
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Gagandeep Kaur, Vedant Pinjarkar, Rutuja Rajendra, Latika Pinjarkar, Jaspal Bagga, Poorva Agrawal,. "Deep Learning Model for Retrieving Color Logo Images in Content Based Image Retrieval." Journal of Electrical Systems 20, no. 2s (March 31, 2024): 1325–33. http://dx.doi.org/10.52783/jes.1773.
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Content-Based Image retrieval (CBIR) has gained a magnificent deal of consideration because of the digital image data's epidemic growth. The advancement of deep learning has enabled Convolutional Neural Networks to become an influential technique for extraction of discriminative image features. In recent years, convolutional neural networks (CNNs) have proven extremely effective at extracting unique information from images. In contrast to text-based image retrieval, CBIR gathers comparable images based primarily on their visual content. The use of deep learning, especially CNNs, for feature extraction and image processing has been shown to perform better than other techniques. In the proposed study, we investigate CNNs for CBIR focusing on how well they extract discriminative visual features and facilitate accurate image retrieval. Also Principal Component Analysis and Linear Discriminant Analysis are combined for optimization of features resulting in boosting the retrieval results. Using hierarchical representations learned by CNNs, we aim to improve retrieval accuracy and efficiency. In comparison with conventional retrieval techniques, our proposed CBIR system shows superior performance on a benchmark dataset.
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Naveen, Mr Kommu. "A Review on Content Based Image Retrieval System Features derived by Deep Learning Models." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 42–57. http://dx.doi.org/10.22214/ijraset.2021.39172.
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Abstract: In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally the choice of these features play a very important role in the success of this system, and high level features are required to reduce the “semantic gap”. In this paper, we propose to use features derived from pre-trained network models from a deep- learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method, and also propose a pre-clustering of the database based on the above-mentioned features which yields comparable results in a much shorter time in most of the cases. Keywords Content Based Image Retrieval Feature Selection Deep Learning Pre-trained Network Models Pre-clustering
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Sikandar, Shahbaz, Rabbia Mahum, and AbdulMalik Alsalman. "A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion." Applied Sciences 13, no. 7 (April 4, 2023): 4581. http://dx.doi.org/10.3390/app13074581.
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The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.
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R., Dr Senkamalavalli, and Shammi L. "Content Based Image Retrieval Using Support Vector Machine." International Journal of Innovative Research in Information Security 10, no. 04 (May 8, 2024): 294–99. http://dx.doi.org/10.26562/ijiris.2024.v1004.34.
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A significant issue in multimedia systems is image retrieval. It is identified as the procedure for looking through and obtaining pictures from a dataset. One important and difficult area of study in digital image processing is content-based image retrieval (CBIR). Retrieving pertinent data from a vast picture database after a query image with better system output is a fundamental requirement of the CBIR system. Regretfully, not all techniques can be applied to achieve high retrieval accuracy. Thus, the purpose of this research is to use Support Vector Machine (SVM) to classify query picture data with image database data in order to get similar images. Based on the classification, the accuracy of the image retrieval will be verified.
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Ali et al., Fathala. "Content Based Image Retrieval (CBIR) by Statistical Methods." Baghdad Science Journal 17, no. 2(SI) (June 23, 2020): 0694. http://dx.doi.org/10.21123/bsj.2020.17.2(si).0694.
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An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T- test to measure the independence between more than images, (coefficient of correlate, T- test, Level of significance, find the decision), and, through experimental test, it was found that this proposed method of retrieval technique is powerful than the classical retrieval System.
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Saxena, Priyanka. "An Overview of Feature Extraction Techniques in Content-Based Image Retrieval Systems." International Journal of Current Engineering and Technology 11, no. 01 (January 11, 2021): 1–12. http://dx.doi.org/10.14741/ijcet/v.11.1.1.
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The collection of images has grown rapidly and continues to increase in future due to widespread use of internet. Content based Image Retrieval (CBIR) system aims at retrieval of images of relevance to the query image input by the user from an enormous image database by low-level feature (such as color, texture and shape) extraction from the image. Some common applications of CBIR and existing commercial systems employing CBIR have been highlighted. General algorithm for explaining the working of CBIR is presented. This article presents a review on methods for low level feature extraction like extracting color, texture and edge features. CBIR still faces some challenges like judgement of human perception of visual content, less appropriate selection of similarity measure, semantic gap and other factors.
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Latif, Afshan, Aqsa Rasheed, Umer Sajid, Jameel Ahmed, Nouman Ali, Naeem Iqbal Ratyal, Bushra Zafar, Saadat Hanif Dar, Muhammad Sajid, and Tehmina Khalil. "Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review." Mathematical Problems in Engineering 2019 (August 26, 2019): 1–21. http://dx.doi.org/10.1155/2019/9658350.
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Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.
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Adimas, Adimas, and Suhendro Y. Irianto. "Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval." Scientific Journal of Informatics 8, no. 2 (November 30, 2021): 176–82. http://dx.doi.org/10.15294/sji.v8i2.27865.
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Purpose: Face recognition is a geometric space recording activity that allows it to be used to distinguish the features of a face. Therefore, facial recognition can be used to identify ID cards, ATM card PINs, search for one’s committed crimes, terrorists, and other criminals whose faces were not caught by Close-Circuit Television (CCTV). Based on the face image database and by applying the Content-Base Image Retrieval method (CBIR), committed crimes can be recognized on his face. Moreover, the image segmentation technique was carried out before CBIR was applied. This work tried to recognize an individual who committed crimes based on his or her face by using sketch facial images as a query. Methods: We used an image sketch as a querybecause CCTV could not have caught the face image. The research used no less than 1,000 facial images were carried out, both normal as well asabnormal faces (with obstacles). Findings:Experiments demonstrated good enough in terms of precision and recall, which are 0,8 and 0,3 respectively, which is better than at least two previous works.The work demonstrates a precision of 80% which means retrieval of effectiveness is good enough. The 75 queries were carried out in this work to compute the precision and recall of image retrieval. Novelty: Most face recognition researchers using CBIR employed an image as a query. Furthermore, previous work still rarely applied image segmentation as well as CBIR.
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Kalra, Meenu, and Pooja Handa. "A Survey on Features and Techniques in Content Based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 10 (June 27, 2015): 6129–34. http://dx.doi.org/10.24297/ijct.v14i10.1829.
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Content-based image retrieval (CBIR) is widely adopted method for finding images from vast collection of images in the database. As the collections of images are growing at a rapid rate, demand for efficient and effective tools for retrieval of query images from database is increased significantly. Among them, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images as it requires relatively less human intervention. The requirement for development of CBIR is enhanced due to tremendous growth in volume of images as well as the widespread application in multiple fields. Texture, color, shaped, contours etc are the important entities to represent and search the images. These features of images are extracted and implemented for a similarity check among images. In this paper, we have conducted a survey on the CBIR techniques and its approaches and their usage in various domains.
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Al-Obaide, Zahraa H., and Ayad A. Al-Ani. "COMPARISON STUDY BETWEEN IMAGE RETRIEVAL METHODS." Iraqi Journal of Information and Communication Technology 5, no. 1 (April 29, 2022): 16–30. http://dx.doi.org/10.31987/ijict.5.1.182.
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Searching for a relevant image in an archive is a problematic research issue for the computer vision research community. The majority of search engines retrieve images using traditional text-based approaches that rely on captions and metadata. Extensive research has been reported in the last two decades for content-based image retrieval (CBIR), analysis, and image classification. Content-Based Image Retrieval is a process that provides a framework for image search, and low-level visual features are commonly used to retrieve the images from the image database. The essential requirement in any image retrieval process is to sort the images with a close similarity in terms of visual appearance. The shape, color, and texture are examples of low-level image features. In image classification-based models and CBIR, high-level image visuals are expressed in the form of feature vectors made up of numerical values. The researcher's findings a significant gap between human visual comprehension and image feature representation. In this paper, we plan to present a comparison study and a comprehensive overview of the recent developments in the field of CBIR and image representation.
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Abinaya, S., and T. Rajasenbagam. "Enhanced Visual Analytics Technique for Content-Based Medical Image Retrieval." International Journal of Electrical and Electronics Research 10, no. 2 (June 30, 2022): 93–99. http://dx.doi.org/10.37391/ijeer.100207.
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Content-based image retrieval (CBIR) is a method for searching that finds related images in a medical database. Furthermore, a clinical adaptation of CBIR is hampered in part by a contextual gap that is the disparity among the person characterization of the picture and the framework characterization of the image. This technique makes it tough for the user to validate the fetched images that are similar to the query image in addition to that it only fetches the images of top-ranked and ignores the low-ranking ones. Visual Analytics for Medical Image Retrieval is a novel procedure for medicinal CBIR proposed in this research (VAMIR). By integrating human and machine analysis, Visual Analytics provides the potential to address the above-mentioned significant challenges. The texture properties are retrieved using the shape features extraction and Gray Level Co-occurrence Matrix (GLCM) is performed by contour-based shape descriptor. Using the Euclidean distance correlation metric, related medical pictures will be fetched by distinguishing the query image's attribute vector with the database images' respective attribute vectors. A vector of multiple features outperforms a vector of a single feature in terms of quality. The VAMIR implementation demonstrates that the search outcome for the user is acquired with 90% of recall and precision.
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Hassan, Rasha Qassim, Zainab N. Sultani, and Ban N. Dhannoon. "Content-based image retrieval based on corel dataset using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (December 1, 2023): 1854. http://dx.doi.org/10.11591/ijai.v12.i4.pp1854-1863.
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A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.
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Tarjoman, Mana, Emad Fatemizadeh, and Kambiz Badie. "A FRAMEWORK FOR CONTENT-BASED HUMAN BRAIN MAGNETIC RESONANCE IMAGES RETRIEVAL USING SALIENCY MAP." Biomedical Engineering: Applications, Basis and Communications 25, no. 04 (August 2013): 1350045. http://dx.doi.org/10.4015/s1016237213500452.
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Content-based image retrieval (CBIR) makes use of low-level image features, such as color, texture and shape, to index images with minimal human interaction. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. The saliency map of an image contains important image regions which are visually more conspicuous by virtue of their contrast with respect to surrounding regions. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image's saliency through ants' movements on the image. The textural features are then calculated from the saliency map of the images. The image indexing is done with an adaptive neuro-fuzzy inference system (ANFIS), which can categorize the magnetic resonance images as normal or tumoral. In online image retrieval, a query image is introduced to the system and the system will return the relevant images. The experimental result shows the accuracy of 98.67% for the image retrieval in our proposed system and improves the retrieval efficiency in compare with the classical CBIR systems.
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Fatima, Shaheen. "A Study on Design and Development of Framework for Content-based Image Retrieval." Journal of Advanced Research in Electronics Engineering and Technology 08, no. 3&4 (November 27, 2021): 12–15. http://dx.doi.org/10.24321/2456.1428.202103.
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CBIR is one of the most widely used approaches for detecting images from an extensive image database. The advanced integration and deployment of computer networking technologies enabled a sudden explosion in the number of ever increasing different types of internet based contents eg: digital images, audio and video content etc. Therefore it leads to a situation where retrieval of those complex data in a short period of time become more challenging. As a primary consequence there is an immense need to develop a novel technique well capable of retrieving such complex information based on their respective content or features. Howere, taking above consideration into account. In this paper, we discuss the reviews on the proposed study formulates an efficient Content Based Image Retrieval (CBIR) framework. The framework also implements a conceptual modelling based on biomedical image retrieval and classification. The study outcomes, found to exhibit better accuracy in retrieving similar images with very less processing time.
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Fatima, Shaheen. "A Study on Design and Development of Framework for Content-based Image Retrieval." Journal of Advanced Research in Electronics Engineering and Technology 08, no. 3&4 (November 27, 2021): 12–15. http://dx.doi.org/10.24321/2456.1428.202103.
Full textAbstract:
CBIR is one of the most widely used approaches for detecting images from an extensive image database. The advanced integration and deployment of computer networking technologies enabled a sudden explosion in the number of ever increasing different types of internet based contents eg: digital images, audio and video content etc. Therefore it leads to a situation where retrieval of those complex data in a short period of time become more challenging. As a primary consequence there is an immense need to develop a novel technique well capable of retrieving such complex information based on their respective content or features. Howere, taking above consideration into account. In this paper, we discuss the reviews on the proposed study formulates an efficient Content Based Image Retrieval (CBIR) framework. The framework also implements a conceptual modelling based on biomedical image retrieval and classification. The study outcomes, found to exhibit better accuracy in retrieving similar images with very less processing time.
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Ayan, Mehmet, O. Ayhan Erdem, and Hasan Şakir Bilge. "Multi-Featured Content-Based Image Retrieval Using Color and Texture Features." Applied Mechanics and Materials 850 (August 2016): 136–43. http://dx.doi.org/10.4028/www.scientific.net/amm.850.136.
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Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogram, color moment, GLCM and proposed methods are tested respectively. As a result, it is observed that proposed method which integrates color and texture features gave better results than the other methods used independently. To demonstrate the proposed system is successful, it was compared with existing CBIR systems. The proposed method showed superior performance than other comparative systems.
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Shiral, J. V., Munmun Burman, Apurva Bhadbhade, Dhanashree Patil, Kajal Motghare, and Neha Wanjari. "Retrieval of Images Using SVM." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (March 31, 2015): 106–11. http://dx.doi.org/10.53555/nncse.v2i3.500.
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Image retrieval is a technique which is used to search and retrieve images from a large database of digital images. Content-based image retrieval (CBIR) is a technique which allows searching images from large scale image database based on contents as needed by user.This paper introduces a technique to retrieve images by classifying it on the basis of the features and characteristics it contains using Support Vector Machine (SVM). The dataset of images is created which is used for feature matching purpose by SVM to find similar images from the database and based on user requirements images are retrieved.
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I. Heaven Rose, R., and A. C. Subajini. "An Efficient Medical Content Based Image Retrieval with Lenient Relevance Feedback." International Journal of Engineering & Technology 7, no. 3.6 (July 4, 2018): 175. http://dx.doi.org/10.14419/ijet.v7i3.6.14965.
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Content Based Image Retrieval (CBIR) for medical imageries is still in its early stage. There are many challenging research issues. Retrieve similar images only is the current problem in medical CBIR. One idea to solve this difficult is minimizing the gap among two descriptions i.e. low level extracted features of image and high level human perception of image. There are various Relevance Feedback (WF) methods have been considered to minimize the semantic gap in medical CBIR system. But most of them were deals with hard Feedback. In Hard Feedback system user can interact with the system in one query session. We recommend to aid the usage of lenient relevance response to better capture the intention of users. The meta-knowledge mined from multiple user’s experience be able to increase the precision of subsequent image recovery results. Here we suggest an algorithm to mine lenient association rules from the group of suggestion i.e. image weight value given by the user. To reduce the amount of strong rules we offer two rule lessening techniques related to redundancy detection and confidence quantization. Best first search and Binary search methods are similarly applied to advance the procedure of weight interface. The effectiveness of the offered system is assessed regarding precision and average retrieval time. The experimental results on medical images display that the proposed method is able to improve the accuracy of medical CBIR system and reduces the retrieval time than other usual methods.
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Majeed, Saqib. "Optimization of Content Based Image Retrieval Using Hybrid Approach." Quaid-e-Awam University Research Journal of Engineering, Science & Technology 20, no. 1 (June 30, 2022): 110–20. http://dx.doi.org/10.52584/qrj.2001.14.
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With the dawn of multimedia technology and the social web, retrieval from large image databases become possible. As a consequence of rising usage and incredible enthusiasm for inquiry about on Content-Based Image Retrieval (CBIR) systems, it needs improvement in the accuracy of CBIR systems in addition to the decline in monotonous results. Currently, the majority of research has focused on the representation and differentiation of images by an arrangement of low-level visual features. However, most of the retrieval systems produce repetitive or unnecessary retrieval results, termed a redundancy factor. In addition, content-based retrieval with reduced redundancy has a direct correlation with high-level semantics which is overlooked. Accurate content retrieval with reduced redundancy enables the user to focus on the actual problem in preference to nurture the retrieval results, augments the efficiency, and improves overall system performance. To enhance the retrieval accuracy and diminish the redundancy factor in image retrieval, we proposed an optimization-based technique that is blended with classification. In the proposed novel hybrid approach for CBIR, we used a two-tier architecture model. The first tier belongs to the feature extraction process via Particle Swarm Optimization with a Support Vector Machine as a classifier. On the way to reduce the redundancy factor, the K-Nearest Neighbor as a classifier is used with the Genetic Algorithm in the second tier. We noted a noteworthy increase in retrieval accuracy for images that is up to 25% (approx.). The proposed hybrid model is effective to enhance accuracy and reduce redundancy factors in CBIR systems. We used the WANG dataset for experimentation. Henceforward, improving the retrieval accuracy and reducing redundancy factor using unsupervised learning techniques is part of our future work.
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Mustikasari, Metty, and Sarifuddin Madenda. "Performance Analysis of Color based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 4 (January 20, 2014): 3373–81. http://dx.doi.org/10.24297/ijct.v12i4.7058.
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Recently Content based image retrieval (CBIR) is an active research. This paper proposes a technique to retrieve images based on color feature and evaluate the retrieval system performance. In this retrieval system Euclidean distance and City block distance are used to measure similarity of images. This algorithm is tested by using Corel image database which is provided by James Wang. The performance of retrieval system is measured in terms of its recall and precision. The effectiveness of retrieval system is also measured based on Average Rank (AVRR) of all relevant retrieves images and Ideal Average Rank of relevant images (IAVRR). The experimental results show that city block has achieved higher retrieval performance than Euclidean distance.
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Feng, Yuhu, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama. "Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval." Applied Sciences 13, no. 10 (May 11, 2023): 5948. http://dx.doi.org/10.3390/app13105948.
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Content-based image retrieval (CBIR) aims to find desired images similar to the image input by the user, and it is extensively used in the real world. Conventional CBIR methods do not consider user preferences since they only determine retrieval results by referring to the degree of resemblance or likeness between the query and potential candidate images. Because of the above reason, a “semantic gap” appears, as the model may not accurately understand the potential intention that a user has included in the query image. In this article, we propose a re-ranking method for CBIR that considers a user’s gaze trace as interactive information to help the model predict the user’s inherent attention. The proposed method uses the user’s gaze trace corresponding to the image obtained from the initial retrieval as the user’s preference information. We introduce image captioning to effectively express the relationship between images and gaze information by generating image captions based on the gaze trace. As a result, we can transform the coordinate data into a text format and explicitly express the semantic information of the images. Finally, image retrieval is performed again using the generated gaze-dependent image captions to obtain images that align more accurately with the user’s preferences or interests. The experimental results on an open image dataset with corresponding gaze traces and human-generated descriptions demonstrate the efficacy or efficiency of the proposed method. Our method considers visual information as the user’s feedback to achieve user-oriented image retrieval.
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Yadav, Salandri Abhishesk, Hima Bindu Kunchanapalli, and A. Poornima. "ENHANCING IMAGE RETRIEVAL EFFICIENCY WITH SPATIAL DEPENDENCE MATRIX AND TRANSLATION INVARIANT DISCRETE WAVELET TRANSFORM." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 8 (April 24, 2021): 3312–17. http://dx.doi.org/10.61841/turcomat.v12i8.14345.
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The present study describes the implementation of a highly effective content-based image retrieval (CBIR) system. This system utilizes an integrated approach for feature extraction, incorporating the use of a spatial dependence matrix (SDM) to extract texture features from the provided images. Additionally, a translation invariant discrete wavelet transform (TIDWT) is employed for low-level feature extraction. Moreover, the effectiveness of the proposed hybrid Content-Based Image Retrieval (CBIR) system was evaluated using the Tanimoto distance. The results of a comprehensive experimental investigation reveal that the suggested hybrid Content-Based Image Retrieval (CBIR) system exhibits a significant enhancement in efficiency when compared to standard CBIR systems.
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Zakariya, S. M. "Evaluation of Unsupervised Content-Based Image Retrieval Systems by the Combination of Different Image Features." International Journal of Research in Engineering and Innovation 08, no. 02 (2017): 85–92. http://dx.doi.org/10.36037/ijrei.2017.1213.
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The process of locating pertinent images in the image database is known as an image retrieval system. Text-based and content-based image retrieval are the two main categories of image retrieval methods. The visual characteristics of a picture, such as color, texture, shape, and spatial design, are used in the content-based technique of image retrieval. On the other hand, the images are represented and indexed in a content-based manner. An approach for cluster-based graph partitioning is utilized to obtain the pictures in an unsupervised manner. In this work, many picture properties are fused to compare the performance of different CBIR systems. The performance is assessed with varying degrees of accuracy. A few current CBIR systems with the same meaning are used to compare the accuracy. It is discovered that the accuracy of the CBIR above systems is superior to that of the current systems. A standard database of 1000 identically resolved photos from COREL is used to evaluate the approaches
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Estrela, Vania V. "Content-Based Image Retrieval (CBIR) in Big Histological Image Databases." Medical Technologies Journal 4, no. 3 (December 7, 2020): 581–82. http://dx.doi.org/10.26415/2572-004x-vol4iss3p581-582.
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Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge.
Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management.
Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests.
Conclusion: This research effort significant contributions are twofold. 1) Defining a comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods.
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Shaheen, Fatima, and R. L. Raibagkar. "Efficient Content-Based Image Retrieval System with Two-Tier Hybrid Frameworks." Applied Computer Systems 27, no. 2 (December 1, 2022): 166–82. http://dx.doi.org/10.2478/acss-2022-0018.
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Abstract The Content Based Image Retrieval (CBIR) system is a framework for finding images from huge datasets that are similar to a given image. The main component of CBIR system is the strategy for retrieval of images. There are many strategies available and most of these rely on single feature extraction. The single feature-based strategy may not be efficient for all types of images. Similarly, due to a larger set of data, image retrieval may become inefficient. Hence, this article proposes a system that comprises of two-stage retrieval with different features at every stage where the first stage will be coarse retrieval and the second will be fine retrieval. The proposed framework is validated on standard benchmark images and compared with existing frameworks. The results are recorded in graphical and numerical form, thus supporting the efficiency of the proposed system.
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Khan, Shaziya, and Shamaila Khan. "An Efficient Content based Image Retrieval: CBIR." International Journal of Computer Applications 152, no. 6 (October 17, 2016): 33–37. http://dx.doi.org/10.5120/ijca2016911885.
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Singh, Bohar, and Mrs Mehak Aggarwal. "Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 2 (August 16, 2018): 7215–25. http://dx.doi.org/10.24297/ijct.v17i2.7606.
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Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using KNN algorithm We have used steerable pyramid to extract texture features from query image and classified database images and store them in feature features. Therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time.
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Singh, Bohar, and Mrs Mehak Aggarwal. "A Review On Content Based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 2 (August 16, 2018): 7226–35. http://dx.doi.org/10.24297/ijct.v17i2.7607.
Full textAbstract:
In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s. To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database. These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color. For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing. Since anautomated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.