Neighborhood-based feature weighting for relevance feedback in content-based retrieval

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. In this paper we propose a weighted similarity measure based on the nearest-neighbor relevance feedback technique proposed by the authors. Each image is ranked according to a relevance score depending on nearest-neighbor distances from relevant and non-relevant images. Distances are computed by a weighted measure, the weights being related to the capability of feature spaces of representing relevant images as nearest-neighbors. This approach is proposed to weights individual features, feature subsets, and also to weight relevance scores computed from different feature spaces. Reported results show that the proposed weighting scheme improves the performances with respect to unweighed distances, and to other weighting schemes.