Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Efficient use of local edge histogram descriptor
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Edge-based structural features for content-based image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
A criterion for optimizing kernel parameters in KBDA for image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Use of relevance feedback (RF) in the feature vector model has been one of the most popular approaches for fine tuning query for content-based image retrieval (CBIR) systems. This paper proposes a framework that extends the RF approach to capture the inter-query relationship between current and previous queries. By using the feature vector model, this approach avoids the need of "memorizing" actual retrieval relationship between the actual image indexes and the previous queries. This implies that the approach is more suitable for image database application where images are frequently added or removed. This paper has extended the authors' previous work [1] by applying a semantic structure to connect the previous queries both visually and semantically. In addition, active learning strategy has been used in this paper to explore images that may be semantically similar while visually different.