Large scale image clustering with support vector machine based on visual keywords

Authors:
Tian-Tian Chang;Horace H. S. Ip;Jun Feng
Affiliations:
City University of Hong Kong, China and Centre for Innovative Applications of Internet and Multimedia Technologies (AIMtech), Hong Kong, China;City University of Hong Kong, China and Centre for Innovative Applications of Internet and Multimedia Technologies (AIMtech), Hong Kong, China;City University of Hong Kong, China and Northwest University, China
Venue:
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Year:
2010

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Abstract

Support Vector Machine Clustering (SVMC) is a model-based clustering method designed primarily for solving 2-class clustering problems. In this paper, we generalize the SVMC method to multi-class clustering via two different strategies, namely One-Against-All and hierarchical clustering. We applied the resulting multi-class SVMC techniques to large scale image clustering based on the visual keywords representation and Histogram Intersection Kernel. Experiments on two benchmark databases show that compared with traditional Support Vector Clustering (SVC) method, our proposed approach is particularly suited to large scale data and large number of classes clustering problems, in terms of computational efficiency and clustering quality.