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Journal articles on the topic 'Content-Based Image Retrieval (CBIR)'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Varma, Ankitha, and Dr Kamalpreet Kaur. "Survey on content based image retrieval." International Journal of Engineering & Technology 7, no. 4.5 (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

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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 (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 appli

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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 (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 wa

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Anuradha, Shitole1 and Uma Godase2. "Survey on Content Based Image Retrieval." International Journal of Computer-Aided Technologies (IJCAx) 01, dec (2014): 01–09. https://doi.org/10.5281/zenodo.1450266.

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Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques

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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 (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 feat

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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 (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

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Kaur, Bhupinder. "A Deep Learning Approach for Content-Based Image Retrieval." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50977.

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Content-Based Image Retrieval (CBIR) aims to retrieve relevant images based on visual content rather than metadata, addressing the limitations of traditional retrieval methods. This study proposes a deep learning-based CBIR system utilizing Convolutional Neural Networks (CNNs) for automatic feature extraction. Leveraging the CIFAR-10 dataset, the system is evaluated against traditional handcrafted methods such as color histograms and color moments. Various retrieval paradigms image-based, text-based, sketch-based, and conceptual layout are analyzed for performance comparison. Experimental resu

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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 (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, fe

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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 (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 diffe

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Navdeep, Kaur* Jasdeep Singh Mann2. "CONTENT BASED IMAGE RETRIEVAL USING MULTI SVM AND COLOR AND TEXTURE COMBINATION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 3 (2019): 79–86. https://doi.org/10.5281/zenodo.2595823.

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The dramatic rise in the sizes of images databases has stirred the development of effective and efficient retrieval systems. The development of these systems started with retrieving images using textual connotations but later introduced image retrieval based on content. This came to be known as Content Based Image Retrieval or CBIR. Systems using CBIR retrieve images based on visual features such as texture, color and shape, as opposed to depending on image descriptions or textual indexing. In the proposed work we will use various types of image features like color, texture, shape, energy, amp

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Sheela, Kalavakuri. "Content Based Image Retrieval Using Colour and Shape Features." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 731–35. https://doi.org/10.22214/ijraset.2025.66916.

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Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Content based image retrieval (CBIR) deals with the extraction of implicit knowledge from the image database. Feature selection and extraction is the pre-processing step of CBIR. Obviously this is a critical step in the entire scenario of CBIR. Though there are various features available, the aim is to identify the best features and thereby extract relevant information from the images

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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 (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 patte

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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 (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 wi

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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 (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 wi

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Shabbir, Z., I. Arshad, G. Raja, and A. K. Khan. "Content Based Image Retrieval using Improved Local Tetra Pattern and Neural Network." Nucleus 53, no. 4 (2016): 225–32. https://doi.org/10.71330/thenucleus.2016.117.

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Because of the exponential increase in digital images, image databases have grown to a much large volume that retrieval of required images from these databases is a very difficult task. Image retrieval can also be practiced via human annotation but it cannot be trusted. So, now a days, a more relied and effective method to retrieve relevant images from a large databse is Content Based Image Retrieval (CBIR). These days, main focus is to achieve a more accurate CBIR algorithm so that retrieval efficiency can be increased. In this paper, we have proposed a CBIR algorithm which retrieves images f

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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 (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 Text

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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 (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) l

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Alexander, Harefa Jeklin, Purnama Yudy, and Harvianto. "Wavelet-Based Color Histogram on Content-Based Image Retrieval." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1256–63. https://doi.org/10.12928/TELKOMNIKA.v16i3.7771.

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The growth of image databases in many domains, including fashion, biometric, graphic design, architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used for finding relevant images from those huge and unannotated image databases based on low-level features of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH) on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database containing 1000 color images. The experiment results show that 2nd level WBCH gives be

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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 (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 s

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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 Gradie

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Abdulla P., Shaik, and Abdul Razak T. "Retrieval-Based Inception V3-Net Algorithm and Invariant Data Classification using Enhanced Deep Belief Networks for Content-Based Image Retrieval." Scientific Temper 15, spl-1 (2025): 415–24. https://doi.org/10.58414/scientifictemper.2024.15.spl.48.

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In the present scenario, Content-Based Image Retrieval (CBIR) performs a constantly changing function that makes use gain knowledge from images. Moreover, it is also the dynamic sector of research and was recently rewarded due to the drastic increase in the performance of digital images. To retrieve images from the massive dataset, experts utilize Content Based Image Retrieval. This approach automatically indexes and retrieves images depending upon the contents of the image, and the developing techniques for mining images are based on the CBIR systems. Based on the visual characteristics of th

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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 (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 approa

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Anish, L., and S. Thiyagarajan. "Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval." Indian Journal Of Science And Technology 17, no. 25 (2024): 2610–21. http://dx.doi.org/10.17485/ijst/v17i25.745.

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Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the S

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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 (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

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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 (2012): 149–52. http://dx.doi.org/10.24297/ijct.v3i1c.2768.

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Content- Based Image Retrieval(CBIR) or QBIRÂ is the importantÂ field of research..ContentÂ Based Image retrieval has gained much popularityÂ 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 propertiesÂ over color,Â texture and shape The experiments show the similarity between these features and also that the output obtained using this combination

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GNANAPRIYA, DR S. "AI Image Generating Based on Given Hints." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42122.

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Content-Based Image Retrieval (CBIR) is a method used to retrieve images based on their visual content, including features like color, texture, and shape. Traditional CBIR techniques often rely on handcrafted features, which are limited in their ability to accurately match images in complex or varied datasets. The advent of Convolutional Neural Networks (CNNs) has significantly improved feature extraction, leading to better image retrieval accuracy. However, CNN-based systems can still struggle with issues like high-dimensional feature vectors, noise, and scalability in large datasets. The int

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Manimegalai, A., Prem Kumar Josephine, and Ashwin Nanda. "Deep ensemble architectures with heterogeneous approach for an efficient content-based image retrieval." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4843–55. https://doi.org/10.11591/ijai.v13.i4.pp4843-4855.

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In the field of digital image processing, content-based image retrieval (CBIR) has become essential for searching images based on visual content characteristics like color, shape, and texture, rather than relying on text based annotations. To address the increasing demands for efficiency and precision in CBIR systems, we introduce the HybridEnsembleNet methodology. HybridEnsembleNet combines deep learning algorithms with an asymmetric retrieval framework to optimize feature extraction and comparison in extensive image databases. This novel approach, specifically custom-made for CBIR, employs a

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Bhushit, Chandra Neema*1 &. Khushboo Mandaniya2. "PROPOSED ALGORITHM FOR CONTENT BASED IMAGE RECOGNITION USING ENHANCED K-MEANS CLUSTERING ALGORITHM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 184–90. https://doi.org/10.5281/zenodo.1002701.

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The content based image retrieval (CBIR) is the well-liked and heart favorite area of research in the field of digital image processing. The key goal of content based image retrieval (CBIR) is to excerpt the visual content of an image directly, like color, texture, or shape. There are several applications of the CBIR technique such as forensic laboratories, crime detection, image searching etc. For the purpose of feature extraction of well-matched images from the database, a universal CBIR system utilizes texture, color and shape based techniques. In this presented work, we have offered an eff

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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 (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 forma

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Gassner, Mathias, Javier Barranco Garcia, Stephanie Tanadini-Lang, 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 pr

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Yinghui, Zhang, Zhang Fengyuan, Cui Yantong, and Ning Ruoci. "CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS." International Journal of Engineering Technologies and Management Research 5, no. 2 (2018): 181–89. https://doi.org/10.5281/zenodo.1186565.

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<strong><em>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 patien

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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 (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 c

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Kamatchi, Chinnathambi, Rathiya Rajendran, Kopperundevi Nagarajan, Brinda Palanisamy, Deepika Jeyabalan, and Rama Subramanian Paperananthamurugesan. "Convolutional neural network-based strategies for efficient content-based image retrieval." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 551. http://dx.doi.org/10.11591/ijeecs.v37.i1.pp551-559.

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Recent years have seen a meteoric rise in the usage of enormous image databases due to advancements in multimedia technologies. One of the most critical technologies for image processing nowadays is image retrieval. This study uses convolutional neural networks (CNNs) for content-based image retrieval (CBIR). With the ever-growing number of digital photos, practical methods for retrieving these images are crucial. CNNs are incredibly efficient in many computer vision applications. Improving the efficacy and precision of image retrieval systems is the primary goal of our research into using dee

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Chinnathambi, Kamatchi Rathiya Rajendran Kopperundevi Nagarajan Brinda Palanisamy Deepika Jeyabalan Rama Subramanian Paperananthamurugesan. "Convolutional neural network-based strategies for efficient content-based image retrieval." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (2025): 551–59. https://doi.org/10.11591/ijeecs.v37.i1.pp551-559.

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Recent years have seen a meteoric rise in the usage of enormous image databases due to advancements in multimedia technologies. One of the most critical technologies for image processing nowadays is image retrieval. This study uses convolutional neural networks (CNNs) for content-based image retrieval (CBIR). With the ever-growing number of digital photos, practical methods for retrieving these images are crucial. CNNs are incredibly efficient in many computer vision applications. Improving the efficacy and precision of image retrieval systems is the primary goal of our research into using dee

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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 (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, first

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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 (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 Br

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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 (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 ex

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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 (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 derive

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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 (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

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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 (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 d

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Nitisha, Soni M.Tech. (IT) Scholar* &. Latika Pinjarkar Sr. Associate Professor (IT). "CONTENT BASED IMAGE RETRIEVAL (CBIR): REVIEW AND CHALLENGES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 171–74. https://doi.org/10.5281/zenodo.805409.

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Content based image retrieval (CBIR) from large resources has become an area of wide interest nowadays in many applications. CBIR is very useful in several applications such as medical imaging, modern diagnosis, remote sensing and satellite imaging. The different types of images are subjected to set of operations used as constituent stages of CBIR. The method was initially used in 1990s and it is an image retrieval method using image vision contents such as color, texture, shape, spatial relationship, not using image notation to search images.Satellite imagery has become an important part of o

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Ali et al., Fathala. "Content Based Image Retrieval (CBIR) by Statistical Methods." Baghdad Science Journal 17, no. 2(SI) (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 ch

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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 (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 datab

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Latif, Afshan, Aqsa Rasheed, Umer Sajid, et al. "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 approache

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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 (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 a

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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 (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 i

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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 (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 v

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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 (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) techniq

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Mr.P.Anand, T.Ajitha, M.Priyadharshini, and M.G.Vaishali. "CONTENT BASED IMAGE RETRIEVAL (CBIR) USING MULTIPLE FEATURES FOR TEXTILE IMAGES BY USING SVM CLASSIFIER." International Journal of Computational Science and Information Technology (IJCSITY) 2, May (2014): 1–10. https://doi.org/10.5281/zenodo.3541590.

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<strong>ABSTRACT </strong> In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a relevant image from an outsized database. Textile images showed the way for the development of CBIR. It establishes the efficient combination of color, shape and texture features. Here the textile image is given as dataset. The images in database are loaded. The resultant image is given as input to feature extraction technique which is transformation of input image into a set of features such as color, texture and shape. The texture feature of an image is taken out

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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 (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

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