Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
ACM Computing Surveys (CSUR)
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Learning Visual Keywords for Content-Based Retrieval
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
Partially supervised clustering for image segmentation
Pattern Recognition
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Query Shifting Based on Bayesian Decision Theory for Content-Based Image Retrieval
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Case-Based Reasoning and the Statistical Challenges
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Research on fuzzy kohonen neural network for fuzzy clustering
CDVE'06 Proceedings of the Third international conference on Cooperative Design, Visualization, and Engineering
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In recent years feedback approaches have been used in relating low-level image features with concepts to overcome the subjective nature of the human image interpretation. Generally, in these systems when the user starts with a new query, the entire prior experience of the system is lost. In this paper, we address the problem of incorporating prior experience of the retrieval system to improve the performance on future queries. We propose a semi-supervised fuzzy clustering method to learn class distribution (meta knowledge) in the sense of high-level concepts from retrieval experience. Using fuzzy rules, we incorporate the meta knowledge into a probabilistic relevance feedback approach to improve the retrieval performance. Results presented on synthetic and real databases show that our approach provides better retrieval precision compared to the case when no retrieval experience is used.