Relevance feedback based on query refining and feature database updating in CBIR system
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Texture Retrieval Effectiveness Improvement Using Multiple Representations Fusion
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Information retrieval from visual databases using multiple representations and multiple queries
Proceedings of the 2009 ACM symposium on Applied Computing
Speed up interactive image retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
IEEE Transactions on Image Processing
Reducing Manual Feedback in a Distributed CBIR System
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Multimodal image retrieval via Bayesian information fusion
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A fuzzy combined learning approach to content-based image retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Graph cuts in content-based image classification and retrieval with relevance feedback
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Using color chains similarities for MLB sports image retrieval
CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control & signal processing
Capturing contextual relationship for effective media search
Multimedia Tools and Applications
Non-metric similarity ranking for image retrieval
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Content-based image retrieval using color and texture fused features
Mathematical and Computer Modelling: An International Journal
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
An improved distance-based relevance feedback strategy for image retrieval
Image and Vision Computing
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An important requirement for constructing effective content-based image retrieval (CBIR) systems is accurate characterization of visual information. Conventional nonadaptive models, which are usually adopted for this task in simple CBIR systems, do not adequately capture all aspects of the characteristics of the human visual system. An effective way of addressing this problem is to adopt a "human-computer" interactive approach, where the users directly teach the system about what they regard as being significant image features and their own notions of image similarity. We propose a machine learning approach for this task, which allows users to directly modify query characteristics by specifying their attributes in the form of training examples. Specifically, we apply a radial-basis function (RBF) network for implementing an adaptive metric which progressively models the notion of image similarity through continual relevance feedback from users. Experimental results show that the proposed methods not only outperform conventional CBIR systems in terms of both accuracy and robustness, but also previously proposed interactive systems.