Abstract
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.
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