Paper:
Language: English:
Content-Based Image Retrieval via Combination of Similarity Measures
Kazushi Okamoto*, Fangyan Dong*, Shinichi Yoshida**,
and Kaoru Hirota*
*Dept. 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
**School of Information, Kochi University of Technology, 185 Tosayamada-cho-Miyanokuchi, Kochi 782-8502, Japan
Received: December 16, 2010
Accepted: March 30, 2011
Keywords: image retrieval, similarity measure, local feature, indexing, retrieval accuracy
Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.6 pp. 687-697, 2011
Abstract
A multiple (dis)similarity measure combination framework via normalization and weighting of measures is proposed to find suitable measure combinations in terms of retrieval accuracy and computational cost. In the combination of Manhattan and Hellinger distances, the computational time is more than 12 times faster and the retrieval accuracy improves or remains at the same level, when compared with Minkowski distance, a measure having the best retrieval accuracy in the single measure scenario. These performances are determined on a visual word based image retrieval system by using the Corel collections. Due to the reduction of computational cost and robustness of retrieval accuracy in this combination, applications include retrieval employing large number of images and categories in a database.
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