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.
Na Wei and Guo-Hua Geng are supported by the Remote Resource Construction project funded by the Ministry of Education of China.
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Wei, N., Celebi, M.E., Geng, G. (2005). Content Based Retrieval and Classification of Cultural Relic Images. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_47
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DOI: https://doi.org/10.1007/11427445_47
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