Dissimilarity Representation in Multi-feature Spaces for Image Retrieval

Abstract

Abstract. In this paper we propose a novel approach to combine information form multiple high-dimensional feature spaces, which allows reducing the computational time required for image retrieval tasks. Each image is represented in a "(dis)similarity space", where each component is computed in one of the low-level feature spaces as the (dis)similarity of the image from one reference image. This new representation allows the distances between images belonging to the same class being smaller than in the original feature spaces. In addition, it allows computing similarities between images by taking into account multiple characteristics of the images, and thus obtaining more accurate retrieval results. Reported results show that the proposed technique allows attaining good performances not only in terms of precision and recall, but also in terms of the execution time, if compared to techniques that combine retrieval results from different feature spaces.

Information fusion in content based image retrieval: A comprehensive overview

“…This kind of approaches could be categorized as sequential fusion. This paradigm can be used for combining different feature modalities [54], or simply different visual feature sets [55,56]. In other approaches, global and local image descriptors are used sequentially, the first ones performing a coarse similarity search, the latter ones, to refine the search [57,58].…”

“…In addition, [56] propose another use of the dissimilarity representation for improving the performances of relevance feedback approaches based on the Nearest-Neighbor approach [79]. Instead of computing (dis)similarities by using different prototypes (e.g., the relevant images) and a single feature space, the authors propose to compute similarities by using just one prototype, and multiple feature representations.…”