Automatic difference measure between movies using dissimilarity measure fusion and rank correlation coefficients

Abstract

When considering multimedia database growth, one current challenging issue is to design accurate navigation tools. End user basic needs, such as exploration, similarity search and favorite suggestions, lead to investigate how to find semantically resembling media. One way is to build numerous continuous dissimilarity measures from low-level image features. In parallel, an other way is to build discrete dissimilarities from textual information which may be available with video sequences. However, how such different measures should be selected as relevant and be fused ? To this aim, the purpose of this paper is to compare all those various dissimilarities and to propose a suitable ranking fusion method for several dissimilarities. Subjective tests with human observers on the CITIA animation movie database have been carried out to validate the model.

Semi-supervised spectral clustering with automatic propagation of pairwise constraints

“…In the searching for relevant information, user preferences and suggestions can improve the results by adapting them to each personal's interests. When assessing resembling multimedia content, many approaches have been explored to measure the similarity between images [1] and video [2]. In this paper, we explore these concepts in the context of clustering techniques.…”