A multi-instance multi-label scene classification method based on multi-kernel fusion
Tongtong Chen, Liu Chan-juan, Zou Hai-lin, Shusen Zhou, Ying Liu, Xinmiao Ding · 2015
Multi-instance multi-label learning, an extension of multi-instance learning in multi-label classification, has been successfully used in image classification. In existing algorithms, the distribution of instances in bags is generally assumed to be independent of each other, which is difficult to be guaranteed in image classification. Considering instance correlations in a bag, in this paper a novel method of scene classification based on multi-kernel fusion and multi-instance multi-label learning is proposed. First, instance correlations are introduced by means of building graph. Then, different kernel matrices can be derived from kernel functions based on graphs in different scales. Finally, the multi-label can be predicted by the multi-kernel SVM classifier based on multiple-kernel fusion. Experimental results on scene data set and MSRC v2 data set show that the proposed method greatly improves the accuracy of the image scene classification compared with other methods.