Optimal recursive similarity measure estimation for interactive content-based image retrieval

A new recursive algorithm is proposed for optimal estimation of similarity measures used in a content-based retrieval system. This is performed through a relevance feedback mechanism, which adjusts the similarity distance using information fed back to the user according to the relevance of the previously retrieved images. In contrast to conventional relevance feedback schemes to which a degree of importance is assigned to each element of the feature vector describing the image content, the proposed algorithm optimally adapts the similarity measure at each feedback iteration. This is performed by modeling the similarity distance using functional analysis. The algorithm assumes that a small modification of the similarity measure parameters is adequate to adapt the system response to the new user's requirements. In this case, a first-order Taylor series expansion can be applied and a computationally efficient scheme can be implemented to estimate the optimal similarity measure.