Content based retrieval and classification of cultural relic images

Authors:
Na Wei;M. Emre Celebi;Guohua Geng
Affiliations:
Dept. of Computer Science, Northwest University at Xi'an, Shaanxi, China;Dept. of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX;Dept. of Computer Science, Northwest University at Xi'an, Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

In this paper we present a novel system for content-based retrieval and classification of cultural relic images. First, the images are normalized to achieve rotation, translation and scaling invariant similarity retrieval. After normalization, a combination of color and shape features is extracted from the images. In order to improve the retrieval efficiency, a modified version of principal component analysis is used to reduce the dimensionality of the feature space. Retrieval performance of the system is evaluated for three different distance functions using the normalized recall measure. A multi-class support vector machine (SVM) classifier is used for classification. The results demonstrate that the system is both effective and efficient.