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Ok, almost all applications I have seen that use HoG features use linear svm as classifier. Can someone explain for me why linear svm are chosen and why they give good performance?

Are linear svm chosen because it more simple and easier to train than svms that use polynomial or gaussian kernel and using these kernels is not giving significantly better performance?

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I think the reason is that HoG features already have a very high dimensionality. Increasing their dimensionality further by using a kernel simply does not improve accuracy.

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