Relevant bibliographies by topics / Content-Based Image Retrieval (CBIR)

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  2. Dissertations / Theses
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Academic literature on the topic 'Content-Based Image Retrieval (CBIR)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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

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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.

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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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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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Dissertations / Theses on the topic "Content-Based Image Retrieval (CBIR)"

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Macena, Júnior Elias Borges. "Aplicação de técnicas de content-based image retrieval (CBIR) em imagens radiográficas." Universidade Federal de Goiás, 2016. http://repositorio.bc.ufg.br/tede/handle/tede/6405.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
In order to improve the diagnostic process several research centers have focused on the development of information systems applying powerful techniques of computer-aided diagnosis (CAD). In this context, the creation of content-based image retrieval (CBIR) is an important step in developing an efficient CAD system. This work proposes the validation of recovery with a hybrid CBIR method based on 2D medical images. The results of the techniques applied, indicate a hit rate of 90.25% and indicate a gain of 35% in the performance of techniques, that is, the time search and retrieval of images, paving the way for the development of information systems more efficient to build support generic diagnostic systems.
Com o objetivo de melhorar o processo de diagnóstico vários centros de pesquisas têm focado no desenvolvimento de sistemas de informação aplicando poderosas técnicas de diagnóstico auxiliado por computador (Computer-Aided Diagnosis-CAD). Neste contexto, a criação de métodos de recuperação de imagens baseado em conteúdo (Content-based image retrieval - CBIR) é um passo importante para desenvolver um sistema CAD eficiente. Este trabalho propõe a validação de técnicas de recuperação com um método híbrido CBIR baseado em imagens médicas 2D. Os resultados das técnicas aplicadas, indicam uma taxa de acerto de 90,25% e ainda indicam um ganho de 35% no desempenho das técnicas, isto é, no tempo de busca e recuperação das imagens, abrindo caminho para o desenvolvimento de sistemas de informação mais eficientes para construção de sistemas de apoio ao diagnóstico genéricos.
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Larsson, Jimmy. "Taxonomy Based Image Retrieval : Taxonomy Based Image Retrieval using Data from Multiple Sources." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180574.

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With a multitude of images available on the Internet, how do we find what we are looking for? This project tries to determine how much the precision and recall of search queries is improved by using a word taxonomy on traditional Text-Based Image Search and Content-Based Image Search. By applying a word taxonomy to different data sources, a strong keyword filter and a keyword extender were implemented and tested. The results show that depending on the implementation, the precision or the recall can be increased. By using a similar approach on real life implementations, it is possible to force images with higher precisions to the front while keeping a high recall value, thus increasing the experienced relevance of image search.
Med den mängd bilder som nu finns tillgänglig på Internet, hur kan vi fortfarande hitta det vi letar efter? Denna uppsats försöker avgöra hur mycket bildprecision och bildåterkallning kan öka med hjälp av appliceringen av en ordtaxonomi på traditionell Text-Based Image Search och Content-Based Image Search. Genom att applicera en ordtaxonomi på olika datakällor kan ett starkt ordfilter samt en modul som förlänger ordlistor skapas och testas. Resultaten pekar på att beroende på implementationen så kan antingen precisionen eller återkallningen förbättras. Genom att använda en liknande metod i ett verkligt scenario är det därför möjligt att flytta bilder med hög precision längre fram i resultatlistan och samtidigt behålla hög återkallning, och därmed öka den upplevda relevansen i bildsök.
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Voulgaris, Georgios. "Techniques for content-based image characterization in wavelets domain." Thesis, University of South Wales, 2008. https://pure.southwales.ac.uk/en/studentthesis/techniques-for-contentbased-image-characterization-in-wavelets-domain(14c72275-a91e-4ba7-ada8-bdaee55de194).html.

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This thesis documents the research which has led to the design of a number of techniques aiming to improve the performance of content-based image retrieval (CBIR) systems in wavelets domain using texture analysis. Attention was drawn on CBIR in transform domain and in particular wavelets because of the excellent characteristics for compression and texture extraction applications and the wide adoption in both the research community and the industry. The issue of performance is addressed in terms of accuracy and speed. The rationale for this research builds upon the conclusion that CBIR has not yet reached a good performance balance of accuracy, efficiency and speed for wide adoption in practical applications. The issue of bridging the sensory gap, which is defined as "[the difference] between the object in the real world and the information in a (computational) description derived from a recording of that scene." has yet to be resolved. Furthermore, speed improvement remains an uncharted territory as is feature extraction directly from the bitstream of compressed images. To address the above requirements the first part of this work introduces three techniques designed to jointly address the issue of accuracy and processing cost of texture characterization in wavelets domain. The second part introduces a new model for mapping the wavelet coefficients of an orthogonal wavelet transformation to a circular locus. The model is applied in order to design a novel rotation-invariant texture descriptor. All of the aforementioned techniques are also designed to bridge the gap between texture-based image retrieval and image compression by using appropriate compatible design parameters. The final part introduces three techniques for improving the speed of a CBIR query through more efficient calculation of the Li-distance, when it is used as an image similarity metric. The contributions conclude with a novel technique which, in conjunction with a widely adopted wavelet-based compression algorithm, extracts texture information directly from the compressed bit-stream for speed and storage requirements savings. The experimental findings indicate that the proposed techniques form a solid groundwork which can be extended to practical applications.
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Banda, Juan. "Framework for creating large-scale content-based image retrieval system (CBIR) for solar data analysis." Diss., Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/banda/BandaJ0511.pdf.

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With the launch of NASA's Solar Dynamics Observatory mission, a whole new age of high-quality solar image analysis was started. With the generation of over 1.5 Terabytes of solar images, per day, that are ten times higher resolution than high-definition television, the task of analyzing them by scientists by hand is simply impossible. The storage of all these images becomes a second problem of importance due to the fact that there is only one full copy of this repository in the world, therefore an alternate and compressed representation of these images is of vital importance. Current automated image processing approaches in solar physics are entirely dedicated to analyze individual types of solar phenomena and do not allow researchers to conveniently query the whole Solar Dynamics Observatory repository for similar images of their interests. We developed a Content-based Image Retrieval system that can automatically analyze and retrieve multiple different types of solar phenomena, this will fundamentally change the way researchers look for solar images in a similar way as Google changed the way people searched the internet. During the development of our system, we created a framework that would allow researchers to tweak and develop their own content-based image retrieval systems for different domain-specific applications with great ease and a deeper understanding of the representation of domain-specific image data. This framework incorporates many different aspects of image processing and information retrieval such as: image parameter extraction for reduced representation of solar images, image parameter evaluation for validation of image parameters used, evaluation of multiple dissimilarity measures for more accurate data analysis, analyses of dimensionality reduction methods to help reduce storage and processing costs, and indexing and retrieval algorithms for faster and more efficient search. The capabilities of this framework have never been available together as an open source and comprehensive software package. With these unique capabilities, we achieved a higher level of knowledge of our solar data and validated each of our steps into the creation of our solar content-based image retrieval system with an exhaustive evaluation. The contributions of our framework will allow researchers to tweak and develop new content-based image retrieval systems for other domains (e.g astronomy, medical field) and will allow the migration of astrophysics research from the individual analysis of solar phenomenon into larger-scale data analyses.
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Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.

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Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The image retrieval is achieved by transforming images to feature representations in a latent space, where it is possible to reason about image similarity in terms of image content. In this thesis, a self-supervised method is developed to learn feature representations of road scenes images. The self-supervised method learns feature representations for images by adapting intermediate convolutional features from an existing deep Convolutional Neural Network (CNN). A contrastive approach based on Noise Contrastive Estimation (NCE) is used to train the feature learning model. For complex images like road scenes where mutiple image aspects can occur simultaneously, it is important to embed all the salient image aspects in the feature representation. To achieve this, the output feature representation is obtained as an ensemble of feature embeddings which are learned by focusing on different image aspects. An attention mechanism is incorporated to encourage each ensemble member to focus on different image aspects. For comparison, a self-supervised model without attention is considered and a simple dimensionality reduction approach using SVD is treated as the baseline. The methods are evaluated on nine different evaluation datasets using CBIR performance metrics. The datasets correspond to different image aspects and concern the images at different spatial levels - global, semi-global and local. The feature representations learned by self-supervised methods are shown to perform better than the SVD approach. Taking into account that no labelled data is required for training, learning representations for road scenes images using self-supervised methods appear to be a promising direction. Usage of multiple query images to emphasize a query intention is investigated and a clear improvement in CBIR performance is observed. It is inconclusive whether the addition of an attentive mechanism impacts CBIR performance. The attention method shows some positive signs based on qualitative analysis and also performs better than other methods for one of the evaluation datasets containing a local aspect. This method for learning feature representations is promising but requires further research involving more diverse and complex image aspects.
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Weng, Zumao. "Distributed knowledge based image contents retrieval and exploration." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370088.

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Makovoz, Gennadiy. "Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/227.

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The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic similarity. The effectiveness of the LSA retrieval was evaluated based on precision, recall, and F-score. This work extended the application of LSA to high-resolution CT radiology images. The images were chosen for their unique characteristics and their importance in medicine. Because CT images are intensity-only, they carry less information than color images. They typically have greater noise, higher intensity, greater contrast, and fewer colors than a raw RGB image. The study targeted level of intensity for image features extraction. The focus of this work was a formal evaluation of the LSA method in the context of large number of high-resolution radiology images. The study reported on preprocessing and retrieval time and discussed how reduction of the feature set size affected the results. LSA is an information retrieval technique that is based on the vector-space model. It works by reducing the dimensionality of the vector space, bringing similar terms and documents closer together. Matlab software was used to report on retrieval and preprocessing time. In determining the minimum size of concept space, it was found that the best combination of precision, recall, and F-score was achieved with 250 concepts (k = 250). This research reported precision of 100% on 100% of the queries and recall close to 90% on 100% of the queries with k=250. Selecting a higher number of concepts did not improve recall and resulted in significantly increased computational cost.
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Viet, Tran Linh. "Efficient Image Retrieval with Statistical Color Descriptors." Doctoral thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5002.

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Color has been widely used in content-based image retrieval (CBIR) applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. A distance measure is then used to measure the (dis-)similarity between images based on the descriptions of their color distributions in order to quickly find relevant images. The development and investigation of statistical methods for robust representations of such distributions, the construction of distance measures between them and their applications in efficient retrieval, browsing, and structuring of very large image databases are the main contributions of the thesis. In particular we have addressed the following problems in CBIR. Firstly, different non-parametric density estimators are used to describe color information for CBIR applications. Kernel-based methods using nonorthogonal bases together with a Gram-Schmidt procedure and the application of the Fourier transform are introduced and compared to previously used histogram-based methods. Our experiments show that efficient use of kernel density estimators improves the retrieval performance of CBIR. The practical problem of how to choose an optimal smoothing parameter for such density estimators as well as the selection of the histogram bin-width for CBIR applications are also discussed. Distance measures between color distributions are then described in a differential geometry-based framework. This allows the incorporation of geometrical features of the underlying color space into the distance measure between the probability distributions. The general framework is illustrated with two examples: Normal distributions and linear representations of distributions. The linear representation of color distributions is then used to derive new compact descriptors for color-based image retrieval. These descriptors are based on the combination of two ideas: Incorporating information from the structure of the color space with information from images and application of projection methods in the space of color distribution and the space of differences between neighboring color distributions. In our experiments we used several image databases containing more than 1,300,000 images. The experiments show that the method developed in this thesis is very fast and that the retrieval performance chievedcompares favorably with existing methods. A CBIR system has been developed and is currently available at http://www.media.itn.liu.se/cse. We also describe color invariant descriptors that can be used to retrieve images of objects independent of geometrical factors and the illumination conditions under which these images were taken. Both statistics- and physics-based methods are proposed and examined. We investigated the interaction between light and material using different physical models and applied the theory of transformation groups to derive geometry color invariants. Using the proposed framework, we are able to construct all independent invariants for a given physical model. The dichromatic reflection model and the Kubelka-Munk model are used as examples for the framework. The proposed color invariant descriptors are then applied to both CBIR, color image segmentation, and color correction applications. In the last chapter of the thesis we describe an industrial application where different color correction methods are used to optimize the layout of a newspaper page.

A search engine based, on the methodes discribed in this thesis, can be found at http://pub.ep.liu.se/cse/db/?. Note that the question mark must be included in the address.

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Filardi, Ana Lúcia. "Análise e avaliação de técnicas de interação humano-computador para sistemas de recuperação de imagens por conteúdo baseadas em estudo de caso." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-06122007-123935/.

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A recuperação de imagens baseada em conteúdo, amplamente conhecida como CBIR (do inglês Content-Based Image Retrieval), é um ramo da área da computação que vem crescendo muito nos últimos anos e vem contribuindo com novos desafios. Sistemas que utilizam tais técnicas propiciam o armazenamento e manipulação de grandes volumes de dados e imagens e processam operações de consultas de imagens a partir de características visuais extraídas automaticamente por meio de métodos computacionais. Esses sistemas devem prover uma interface de usuário visando uma interação fácil, natural e atraente entre o usuário e o sistema, permitindo que o usuário possa realizar suas tarefas com segurança, de modo eficiente, eficaz e com satisfação. Desse modo, o design da interface firma-se como um elemento fundamental para o sucesso de sistemas CBIR. Contudo, dentro desse contexto, a interface do usuário ainda é um elemento constituído de pouca pesquisa e desenvolvimento. Um dos obstáculos para eficácia de design desses sistemas consiste da necessidade em prover aos usuários uma interface de alta qualidade para permitir que o usuário possa consultar imagens similares a uma dada imagem de referência e visualizar os resultados. Para atingir esse objetivo, este trabalho visa analisar a interação do usuário em sistemas de recuperação de imagens por conteúdo e avaliar sua funcionalidade e usabilidade, aplicando técnicas de interação humano-computador que apresentam bons resultados em relação à performance de sistemas com grande complexidade, baseado em um estudo de caso aplicado à medicina
The content-based image retrieval (CBIR) is a challenging area of the computer science that has been growing in a very fast pace in the last years. CBIR systems employ techniques for extracting features from the images, composing the features vectores, and store them together with the images in data bases management system, allowing indexing and querying. CBIR systems deal with large volumes of images. Therefore, the feature vectors are extracted by automatic methods. These systems allow to query the images by content, processing similarity queries, which inherently demands user interaction. Consequently, CBIR systems must pay attention to the user interface, aiming at providing friendly, intuitive and attractive interaction, leading the user to do the tasks efficiently, getting the desired results, feeling safe and fulfilled. From the points highlighted beforehand, we can state that the human-computer interaction (HCI) is a key element of a CBIR system. However, there is still little research on HCI for CBIR systems. One of the requirements of HCI for CBIR is to provide a high quality interface to allow the user to search for similar images to a given query image, and to display the results properly, allowing further interaction. The present dissertation aims at analyzing the user interaction in CBIR systems specially suited to medical applications, evaluating their usability by applying HCI techniques. To do so, a case study was employed, and the results presented
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Henrysson, Jennie, Kristina Johansson, and Charlotte Juhlin. "Vad säger bilden? : En utvärdering av återvinningseffektiviteten i ImBrowse." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18375.

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The aim of this master thesis is to evaluate the performance of the content-based image retrieval system ImBrowse from a semantic point of view. Evaluation of retrieval performance is a problem in content-based image retrieval (CBIR). There are many different methods for measuring the performance of content-based image retrieval systems, but no common way for performing the evaluation. The main focus is on image retrieval regarding the extraction of the visual features in the image, from three semantic levels. The thesis tries to elucidate the semantic gap, which is the problem when the systems extraction of the visual features from the image and the user’s interpretation of that same information do not correspond. The method of this thesis is based on similar methods in evaluation studies of CBIR systems. The thesis is an evaluation of ImBrowse’s feature descriptors for 30 topics at three semantic levels and compared the descriptors performance based on our relevance assessment. For the computation of the results the precision at DCV = 20 is used. The results are presented in tables and a chart. The conclusion from this evaluation is that the retrieval effectiveness from a general point of view did not meet the semantic level of our relevance assessed topics. However, since the thesis do not have another system with the same search functions to compare with it is difficult to draw a comprehensive conclusion of the results.
Uppsatsnivå: D
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Books on the topic "Content-Based Image Retrieval (CBIR)"

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Eakins, J. P. Content-based image retrieval. Manchester: JISC Technology Applications Pogramme, 1999.

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Tyagi, Vipin. Content-Based Image Retrieval. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6759-4.

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Eakins, John. Content-based image retrieval. Manchester: Joint Information Systems Committee, 1999.

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Marques, Oge, and Borko Furht. Content-Based Image and Video Retrieval. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0987-5.

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Marques, Oge. Content-based image and video retrieval. Boston: Kluwer Academic Publishers, 2002.

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Exploratory image databases: Content-based retrieval. San Diego: Academic Press, 2001.

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1953-, Deb Sagarmay, ed. Multimedia systems and content-based image retrieval. Hershey, PA: Idea Group Publishing, 2004.

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Kushki, Azadeh. An interactive framework for content-based image retrieval. Ottawa: National Library of Canada, 2003.

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1965-, Ma Zongmin, ed. Artificial intelligence for maximizing content based image retrieval. Hershey PA: Information Science Reference, 2009.

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Tong, Zhang, and Kuo C. C. Jay, eds. Content-based audio classification and retrieval for audiovisual data parsing. Boston: Kluwer Academic, 2001.

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Book chapters on the topic "Content-Based Image Retrieval (CBIR)"

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Agrawal, Deepti, Apurva Agarwal, and Dilip Kumar Sharma. "Content-Based Image Retrieval (CBIR): A Review." In Lecture Notes in Electrical Engineering, 439–52. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8892-8_33.

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Prasanthi, B., P. Suresh, and D. Vasumathi. "Index-Based Image Retrieval-Analyzed Methodologies in CBIR." In Lecture Notes in Networks and Systems, 233–42. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3935-5_24.

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Chandana, P., P. Srinivas Rao, C. H. Satyanarayana, Y. Srinivas, and A. Gauthami Latha. "An Efficient Content-Based Image Retrieval (CBIR) Using GLCM for Feature Extraction." In Advances in Intelligent Systems and Computing, 21–30. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3779-5_4.

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Estrela, Vania V., Abdullah Ayub Khan, Aftab Ahmed Shaikh, Asif Ali Laghari, Mazhar Ali Dootio, Mudassir Hussain, Awais Khan Jumani, and Rukhsar Ayub. "Some Issues Regarding Content-Based Image Retrieval (CBIR) for Remote Healthcare Theradiagnosis." In Intelligent Healthcare Systems, 110–34. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003196822-7.

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Haruechaiyasak, Choochart, and Chaianun Damrongrat. "Improving Social Tag-Based Image Retrieval with CBIR Technique." In The Role of Digital Libraries in a Time of Global Change, 212–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13654-2_26.

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Ramamurthy, B., K. R. Chandran, V. R. Meenakshi, and V. Shilpa. "CBMIR: Content Based Medical Image Retrieval System Using Texture and Intensity for Dental Images." In Eco-friendly Computing and Communication Systems, 125–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32112-2_16.

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Zhang, Yu-Jin. "Content-Based Retrieval." In Handbook of Image Engineering, 1513–48. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5873-3_44.

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Furht, Borko, Stephen W. Smoliar, and HongJiang Zhang. "Content-Based Image Retrieval." In Video and Image Processing in Multimedia Systems, 225–70. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2277-5_11.

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da Silva Torres, Ricardo, Nádia P. Kozievitch, Uma Murthy, and Alexandre X. Falcão. "Content-Based Image Retrieval." In Digital Library Applications, 1–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-02284-5_1.

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Wang, Xiaoling, and Kanglin Xie. "Fuzzy Logic-Based Image Retrieval." In Content Computing, 241–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30483-8_29.

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Conference papers on the topic "Content-Based Image Retrieval (CBIR)"

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Khan, Sumaira Muhammad Hayat, Ayyaz Hussain, and Imad Fakhri Taha Alshaikhli. "Comparative Study on Content-Based Image Retrieval (CBIR)." In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE, 2012. http://dx.doi.org/10.1109/acsat.2012.40.

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Valem, Lucas Pascotti, and Daniel Carlos Guimarães Pedronette. "Unsupervised Selective Rank Fusion on Content-Based Image Retrieval." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8303.

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Mainly due to the evolution of technologies to store and share images, the growth of image collections have been remarkable for years. Therefore, developing effective methods to index and retrieve such extensive available visual information is indispensable. The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual descriptors and machine learning methods. Despite the relevant advances in the area, mainly driven by deep learning technologies, accurately computing the similarity between images remains a complex task in various scenarios due to the well known semantic gap problem. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the evaluated scenarios.
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Valem, Lucas Pascotti, and Daniel Carlos Guimarães Pedronette. "Unsupervised Selective Rank Fusion for Content-based Image Retrieval." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2020.11370.

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The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual features and machine learning methods. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task, especially when no training data is available. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the scenarios.
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Fachrurrozi, Muhammad, Erwin, Saparudin, and Mardiana. "Multi-object face recognition using Content Based Image Retrieval (CBIR)." In 2017 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2017. http://dx.doi.org/10.1109/icecos.2017.8167132.

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Sardey, M. P., and M. P. Dale. "Interactive retrieval relevance feedback approach - a tool for content based image retrieval (CBIR)." In National Conference on Signal and Image Processing Applications. IET, 2009. http://dx.doi.org/10.1049/ic.2009.0166.

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Wankhede, Vrushali A., and Prakash S. Mohod. "Content-based image retrieval from videos using CBIR and ABIR algorithm." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342767.

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Deekshatulu, B. L. "Learning Semantics in Content Based Image Retrieval (CBIR) - A Brief Review." In 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems (VCON). IEEE, 2010. http://dx.doi.org/10.1109/vcon.2010.22.

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Sasheendran, N., and C. Bhuvaneswari. "An effective CBIR (Content Based Image Retrieval) approach using Ripplet transforms." In 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE, 2013. http://dx.doi.org/10.1109/iccpct.2013.6528985.

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Stefan, Radu Andrei, Ildiko-Angelica Szoke, and Stefan Holban. "Hierarchical clustering techniques and classification applied in Content Based Image Retrieval (CBIR)." In 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2015. http://dx.doi.org/10.1109/saci.2015.7208188.

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Jadhav, Sunita Manoj, and Vikram Patil. "An effective content Based Image Retrieval (CBIR) system based on evolutionary programming (EP)." In 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). IEEE, 2012. http://dx.doi.org/10.1109/icaccct.2012.6320793.

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Reports on the topic "Content-Based Image Retrieval (CBIR)"

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Rigotti, Christophe, and Mohand-Saïd Hacid. Representing and Reasoning on Conceptual Queries Over Image Databases. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.89.

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The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image object's characteristics, we propose a model which consists of three layers: (1) Feature and Content Layer, intended to contain image visual features such as contours, shapes,etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation, and they are well suited for the design, verification and complexity analysis of algorithms. As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language. The algorithm we propose is sound and complete and relatively efficient.
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Rigotti, Christophe, and Mohand-Saïd Hacid. Representing and Reasoning on Conceptual Queries Over Image Databases. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.89.

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The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image object's characteristics, we propose a model which consists of three layers: (1) Feature and Content Layer, intended to contain image visual features such as contours, shapes,etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation, and they are well suited for the design, verification and complexity analysis of algorithms. As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language. The algorithm we propose is sound and complete and relatively efficient.
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Decleir, Cyril, Mohand-Saïd Hacid, and Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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