Relevance feedback for shape query refinement

In this paper we propose to incorporate a feedback loop, into the ordinal correlation framework and apply it to shape-based image retrieval. The user's feedback on the relevance of the retrieval results is used to tune the weights of the similarity measure. Statistics from the features of both relevant and irrelevant items are used to estimate the weights. Moreover, the information accumulated from previous retrieval iterations is used in the weights estimation. A simple measure of the discrimination power is proposed and used to show that the relevance feedback increases the capability of the ordinal correlation scheme to discriminate between relevant and irrelevant objects.