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Relevant bibliographies by topics / Relevance feedback (RF)

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Academic literature on the topic 'Relevance feedback (RF)'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Relevance feedback (RF)"

1

Imran, Muhammad, Rathiah Hashim, Abd Khalid Noor Elaiza, and Aun Irtaza. "Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/752090.

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One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM

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Smita Selot, Latika Pinjarkar, Manisha Sharma,. "IMPROVED SYSTEM FOR RETRIEVAL OF COLOR LOGO IMAGES USING PSO, SOM AND RELEVANCE FEEDBACK TECHNIQUE." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 634–39. http://dx.doi.org/10.17762/itii.v9i1.181.

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The colour logo identification has one of the key problems of bridging the difference between low-level characteristics and high-level semantics, as the consumer wants to. Relevance feedback (RF) along with self-organizing map (SOM) have been successfully introduced to resolve this void. However, the efficiency of the automated map (SOM) based RF is always low when the feedback sample is limited. This paper proposed a new technology, namely the SOM-SOM-RF that combines SOM-based RF with warm particle optimization, to boost RF performance (PSO). The aim of this proposed technology is to increas

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Yu, Ning, Kien A. Hua, and Danzhou Liu. "Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment." International Journal of Multimedia Data Engineering and Management 2, no. 2 (2011): 42–53. http://dx.doi.org/10.4018/jmdem.2011040103.

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During the last decade, high quality (i.e. over 1 megapixel) built-in cameras have become standard features of handheld devices. Users can take high-resolution pictures and share with friends via the internet. At the same time, the demand of multimedia information retrieval using those pictures on mobile devices has become an urgent problem to solve, and therefore attracts attention. A relevance feedback information retrieval process includes several rounds of query refinement, which incurs exchange of images between the mobile device and the server. With limited wireless bandwidth, this proce

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4

Dong, Yu Bing, Ming Jing Li, and Bai Chuan Li. "Research on Related Technology of Content-Based Image Retrieval." Applied Mechanics and Materials 448-453 (October 2013): 3616–20. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3616.

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Content-Based Image Retrieval (CBIR) system existed a gap between high-level concepts and low-level features. As an effective solution, the Relevance Feedback (RF) technique has been used on many CBIR systems to improve the retrieval precision. In order to further improve convergence speed and retrieval accuracy, a novel relevance feedback method was proposed. According to feedback from user, image feature was weighted and adjusted in the novel method.

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Putzu, Lorenzo, Luca Piras, and Giorgio Giacinto. "Convolutional neural networks for relevance feedback in content based image retrieval." Multimedia Tools and Applications 79, no. 37-38 (2020): 26995–7021. http://dx.doi.org/10.1007/s11042-020-09292-9.

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Abstract Given the great success of Convolutional Neural Network (CNN) for image representation and classification tasks, we argue that Content-Based Image Retrieval (CBIR) systems could also leverage on CNN capabilities, mainly when Relevance Feedback (RF) mechanisms are employed. On the one hand, to improve the performances of CBIRs, that are strictly related to the effectiveness of the descriptors used to represent an image, as they aim at providing the user with images similar to an initial query image. On the other hand, to reduce the semantic gap between the similarity perceived by the u

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R, Sridevi. "Partial Highest Possible Edge Analysis for Interactive Image Accessibility." International Journal of Computer Science and Engineering Communications 1, no. 1 (2013): 13–20. https://doi.org/10.5281/zenodo.821738.

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Relevance feedback is a technique that takes advantage of human-computer interaction to refine high level queries represented by low level features. Among RF schemes, the most popular technique is SVM based RF scheme. When SVM is used as a classifier in RF, there are two strategies. One strategy is to display the most positive images and use them as the training samples. The most-positive images are chosen as the ones farthest from the boundary on the positive side, plus those nearest from the boundary on the negative side if necessary. Another strategy is that most of SVM based RF scheme does

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Kojic, Nenad, Slobodan Cabarkapa, Goran Zajic, and Branimir Reljin. "Implementation of neural network in CBIR systems with relevance feedback." Journal of Automatic Control 16, no. 1 (2006): 41–45. http://dx.doi.org/10.2298/jac0601041k.

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A content-based image retrieval system where an active learning strategy is used to gain relevance feedback (RF) is described. In this way retrieving process may be highly accelerated without significant degradation of accuracy Searching procedure was performed through the two basic steps: an objective one, based on the Euclidean distances and a subjective one based on the user's relevance feedback. Images recognized from user as the best matched to a query are labeled and used for updating the query feature vector through a RBF (radial basis function) neural network. In this process user chan

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Lv, Fei Ya, Xiao Hui Yang, and Deng Feng Li. "Relevance Feedback Based on Particle Swarm Optimize Weight-Vector for Image Retrieval." Advanced Materials Research 989-994 (July 2014): 3579–82. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3579.

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As the physical meaning of components are different in feature vector, this paper presents a weight query vector based on QPM to represent the user’s true intention more properly, and then proposes two RF frameworks to learn the weights for positives and negatives in the feedback process of CBIR by PSO. Experiments were conducted to validate the proposed frameworks based on color histogram weight-vector. The proposed frameworks were compared and outperformed four other relevance feedback methods regarding their efficiency and effectiveness, thanks to the fact that they can make full use of the

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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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Wang, Xue Feng, and Xing Su Chen. "A Two-Stage of Relevance Feedback for Content-Based Image Retrieval." Key Engineering Materials 467-469 (February 2011): 1627–32. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1627.

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In this paper, an effective relevance feedback (RF) approach is proposed in content-based image retrieval (CBIR). In the first stage, according to the user’s marked images, we get theirs predictive probabilities based-on Bayesian methodology which yields the posteriori of the images in the database; second via justify the weight of elements in each feature extracted of images, we refine features by logistic regression with positive features which get from the first stage. Then we rank the images according to the probability of the images in the database. The retrieval system is repeating until

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Book chapters on the topic "Relevance feedback (RF)"

1

Grigorova, Anelia, and Francesco G. B. De Natale. "Semi-automatic Feature-Adaptive Relevance Feedback (SA-FR-RF) for Content-Based Image Retrieval." In Visual Information and Information Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11590064_14.

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Yu, Ning, Kien A. Hua, and Danzhou Liu. "Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment." In Multimedia Data Engineering Applications and Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2940-0.ch010.

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During the last decade, high quality (i.e. over 1 megapixel) built-in cameras have become standard features of handheld devices. Users can take high-resolution pictures and share with friends via the internet. At the same time, the demand of multimedia information retrieval using those pictures on mobile devices has become an urgent problem to solve, and therefore attracts attention. A relevance feedback information retrieval process includes several rounds of query refinement, which incurs exchange of images between the mobile device and the server. With limited wireless bandwidth, this proce

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3

Yu, Ning, Kien A. Hua, and Danzhou Liu. "Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment." In Wireless Technologies. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-101-6.ch314.

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During the last decade, high quality (i.e. over 1 megapixel) built-in cameras have become standard features of handheld devices. Users can take high-resolution pictures and share with friends via the internet. At the same time, the demand of multimedia information retrieval using those pictures on mobile devices has become an urgent problem to solve, and therefore attracts attention. A relevance feedback information retrieval process includes several rounds of query refinement, which incurs exchange of images between the mobile device and the server. With limited wireless bandwidth, this proce

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4

Reljin Branimir, Zajić Goran, Reljin Nikola, and Reljin Irini. "Adaptive Clustering of Image Database (ACID) as an Efficient Tool for Improving Retrieval in a CBIR System." In Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-086-4-172.

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The paper describes a content-based image retrieval (CBIR) system with relevance feedback (RF). Instead of standard relevance feedback procedure, an adaptive clustering of image database (ACID) according to particular subjective needs is introduced in our system. Images labeled by the user as relevant are collected in clusters, and their representative members are used in further searching procedure instead of all images contained in the database. By this way, some history of previous retrieving is embedded into a searching process enabling faster and more subjective retrieval. Moreover, clust

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Zhang, Chengcui, Liping Zhou, Wen Wan, Jeffrey Birch, and Wei-Bang Chen. "An Image Clustering and Feedback-Based Retrieval Framework." In Methods and Innovations for Multimedia Database Content Management. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1791-9.ch005.

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Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In t

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Conference papers on the topic "Relevance feedback (RF)"

1

Grigorova, Anelia I., and Francesco G. B. De Natale. "Feature-adaptive relevance feedback (FA-RF) for content-based image retrieval." In Electronic Imaging 2004, edited by Minerva M. Yeung, Rainer W. Lienhart, and Chung-Sheng Li. SPIE, 2003. http://dx.doi.org/10.1117/12.526654.

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Peng, Zhao-hui, Jun Zhang, Shan Wang, Chang-liang Wang, and Li-zhen Cui. "VSM-RF: A method of relevance feedback in Keyword Search over Relational Databases." In 2009 IEEE International Symposium on IT in Medicine & Education (ITME2009). IEEE, 2009. http://dx.doi.org/10.1109/itime.2009.5236323.

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