large scale image clustering with support vector machine based on visual keywords
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.
image clustering clustering support vector machine visual keywords multi-class one-against-all hierarchical histogram intersection kernel