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Negative Relevance Feedback for Improving Retrieval in Large-Scale Image Collections

Retrieval engines provide results according to user request. Nevertheless, reaching satisfaction can not be guaranteed with simple retrieval step. Therefore, it is necessary to communicate this dissatisfaction to the system through relevance feedback techniques. Indeed, with the growing number of image collections and by applying approximate nearest neighbor (ANN) algorithms to resolve the curse of dimensionality, the semantic gap may increase. For this reason, an additional step of relevance feedback is needful to add semantics to the next retrieval iterations. In this paper, a classification of the different relevance feedback techniques related to region-based image retrieval applications is elaborated. Moreover, a new technique of relevance feedback based on re-weighting regions of the query-image by selecting a set of negative examples is detailed. Furthermore, the general context to carry out this technique which is the large-scale heterogeneous image collections indexing and retrieval is presented. In fact, the main contribution of the proposed work is affording efficient results with the minimum number of relevance feedback iterations for high dimensional image databases. Experiments and assessments are carried out within an RBIR system for "Wang" data set in order to prove the effectiveness of the proposed approaches.