The selection of appropriate proximity measures is one of the crucial success factors of content-based visual information retrieval. In this area of research, proximity measures are used to estimate the similarity of media objects by the distance of feature vectors. The research focus of this work is the identification of proximity measures that perform better than the usual choices (e.g. Minkowski metrics). We evaluate a catalogue of 37 measures that are picked from various areas (psychology, sociology, economics, etc.). The evaluation is based on content-based MPEG-7 descriptions of carefully selected media collections. Unfortunately, some proximity measures are only defined on predicates (e.g. most psychological measures). One major contribution of this paper is a model that allows for the application of such measures on continuous feature data. The evaluation results uncover proximity measures that perform better than others on content-based features. Some predicate-based measures clearly outperform the frequently used distance norms. Eventually, the discussion of the evaluation leads to a catalogue of mathematical terms of successful retrieval and browsing measures.
H. Eidenberger: "Evaluation and Analysis of Similarity Measures for Content-based Visual Information Retrieval"; ACM Multimedia Systems Journal, 3 (2006).
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