ADAPTIVE HIERARCHICAL MULTI-CLASS SVM CLASSIFIER FOR TEXTURE-BASED IMAGE CLASSIFICATION (FriAmOR5)

Author(s) : Song Liu (Nanyang Technological University, Singapore) Haoran Yi (Nanyang Technological University, Singapore) Liang-Tien Chia (Nanyang Technological University, Singapore) Deepu Rajan (Nanyang Technological University, Singapore)

Abstract : In this paper, we present a new classification scheme based on Support Vector Machines (SVM) and a new texture feature, called texture correlogram, for high-level image classification. Originally, SVM classifier is designed for solving only binary classification problem. In order to deal with multiple classes, we present a new method to dynamically build up a hierarchical structure from the training dataset. The texture correlogram is designed to capture spatial distribution information. Experimental results demonstrate that the proposed classification scheme and texture feature are effective for high-level image classification task and the proposed classification scheme is more efficient than the other schemes while achieving almost the same classification accuracy. Another advantage of the proposed scheme is that the underlying hierarchical structure of the SVM classification tree manifests the interclass relationships among different classes.

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