blur detection in a single image is challenging especially when the blur is spatially-varying. developing discriminative blur features is an open problem. in this paper, we propose a new kernel-specific feature vector consisting of the information of a blur kernel and the information of an image patch. specifically, the kernel specific-feature is composed of the multiplication of the variance of filtered kernel and the variance of filtered patch gradients. the feature origins from a blur-classification theorem and its discrimination can also be intuitively explained. to make the kernel-specific features useful for real applications, we build a pool of kernels consisting of motion-blur kernels, defocus-blur (out-of-focus) kernels, and their combinations. by extracting such features followed by the classifiers, the proposed algorithm outperforms the state-of-the-art blur detection method. experimental results on public databases demonstrate the effectiveness of the proposed method.