Back-propagation algorithm for relevance feedback in image retrieval

Content-based image retrieval (CBIR) usually relies on pre-attentive similarities. Results are often coarse because of the gap between the pre-attentive level and the semantic level of the user\´s request. The aim of relevance feedback is to refine results by taking user\´s expertise into account. This paper presents a new feedback architecture for CBIR. Images are compared through a weighted dissimilarity function which can be represented as a "network of dissimilarities". The weights are updated via an error backpropagation algorithm using the user\´s annotations of the successive set of result images. It allows an iterative refinement of the search through a simple interactive process (the user has just to specify if images are relevant or not). A quality assessment realized with three databases containing about 10,000 images shows the performance improvement after feedback