It is a fairly challenging issue to make image repositories easy to be searched and browsed. This depends on a technique--image clustering. Kernel-based clustering algorithm has been one of the most promising clusteri...It is a fairly challenging issue to make image repositories easy to be searched and browsed. This depends on a technique--image clustering. Kernel-based clustering algorithm has been one of the most promising clustering methods in the last few years, beeanse it can handle data with high dimensional complex structure. In this paper, a kernel fuzzy learning （KFL） algorithm is proposed, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method to execute the image clustering automatically. Experimental results show that KFL is a more efficient method for image clustering in comparison with recent renorted alternative methods.展开更多
基金Supported by the National Natural Science Foundation of China （No. 61101159, 60872123）, the China Postdoctoral Science Foundation （No. 20100480049） and the Fundamental Research Funds for the Central Universities （No. 201 IZM0033）
文摘It is a fairly challenging issue to make image repositories easy to be searched and browsed. This depends on a technique--image clustering. Kernel-based clustering algorithm has been one of the most promising clustering methods in the last few years, beeanse it can handle data with high dimensional complex structure. In this paper, a kernel fuzzy learning （KFL） algorithm is proposed, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method to execute the image clustering automatically. Experimental results show that KFL is a more efficient method for image clustering in comparison with recent renorted alternative methods.
