Back-propagation algorithm for relevance feedback in image retrieval
Jérôme Fournier, Matthieu Cord, Sylvie Philipp‐Foliguet · 2002
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.