Learning to Combine Kernels for Object Categorization

Deyuan Zhang, Bingquan Liu, Chengjie Sun, Xiaolong Wang


Kernel classifiers based on the hand-crafted image descriptors proposed in the literature have achieved state-of-the-art results in several dataset and been widely used in image classification systems. Due to the high intra-class and inter-class variety of image categories, no single descriptor could be optimal in all situations. Combining multiple descriptors for a given task is a way to improve the accuracy of the image classification systems. In this paper, we propose a filter framework “Learning to Align the Kernel to its Ideal Form(LAKIF)” to automatically learn the optimal linear combination of multiple kernels. Given the image dataset and the kernels computed on the image descriptors, the optimal kernel weight is learned before the classification. Our method effectively learns the kernel weights by aligning the kernels to their ideal forms, leading to quadratic programming solution. The method takes into account the variation of kernel matrix and imbalanced dataset, which are common in real world image categorization tasks. Experimental results on Graz-01 and Caltech-101 image databases show the effectiveness and robustness of our method.

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DOI: https://doi.org/10.5539/cis.v4n3p116

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