Reducing User Log size in an Inter-Query Learning Content Based Image Retrieval (CBIR) System with a Cluster Merging approach

Use of relevance feedback (RF) in the feature vector model has been one of the most popular approaches to fine tune 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 and removed. The proposed inter-query relationship is presented using a data cluster that is defined by a transformation matrix, a centroid point and the reference boundary value. These parameters are captured in a file commonly known as the user log. The file however will grow rapidly after successive retrieval sessions. In order to reduce the size of the user log, this paper introduces a merging approach to combine clusters that are close-by and similar in their characteristics. Experiments have shown that the proposed framework has out performed the short term learning approach and yet without the burden of the complex database maintenance strategies required in long-term learning approach.