JACIII Vol.11 No.3 pp. 301-307
doi: 10.20965/jaciii.2007.p0301


Cross-Resolution Image Similarity Modeling

Mladen Jović, Yutaka Hatakeyama, and Kaoru Hirota

Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

April 21, 2006
July 28, 2006
March 20, 2007
image similarity, modeling, cross-resolution

A cross-resolution image similarity model employing probabilistic interpretations of the similarity values turned into fuzzy values combined with suitable merge functions is presented. Masking the negative particularities of individual region-based image similarity models, five region-based image similarity models are used at the same time when calculating the overall image similarity. By employing aggregation operators, capturing of a variety of conjunctive, disjunctive, and other non-linear combinations of similarity criteria is allowed. Empirical evaluation of the proposed model on four test databases, containing 4,444 images in 150 semantic categories was carried out. The results obtained from the evaluation revealed that cross-resolution image similarity modeling results in optimal retrieval performance. Compared to two well-known image retrieval systems, SIMPLicity and WBIIS, the proposed model brings an increase of 1.7% and 22% respectively in average retrieval precision. The experimental evaluation presented may thus be helpful and suggest possible further improvements can be achieved along the same line of research directions in various computer vision tasks.

Cite this article as:
Mladen Jović, Yutaka Hatakeyama, and Kaoru Hirota, “Cross-Resolution Image Similarity Modeling,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.3, pp. 301-307, 2007.
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