Kernel Descriptors for Visual Recognition

author: Liefeng Bo, Department of Computer Science and Engineering, University of Washington
published: March 25, 2011,   recorded: December 2010,   views: 978
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The design of low-level image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT\cite{Lowe2004Distinctive} and HOG\cite{Dalal2005Histograms}, are the most successful and popular features for visual object and scene recognition. We highlight the kernel view of orientation histograms, and show that they are equivalent to a certain type of match kernels over image patches. This novel view allows us to design a family of kernel descriptors which provide a unified and principled framework to turn pixel attributes (gradient, color, local binary pattern, \etc) into compact patch-level features. In particular, we introduce three types of match kernels to measure similarities between image patches, and construct compact low-dimensional kernel descriptors from these match kernels using kernel principal component analysis (KPCA)\cite{Scholkopf1998Nonlinear}. Kernel descriptors are easy to design and can turn any type of pixel attribute into patch-level features. They outperform carefully tuned and sophisticated features including SIFT and deep belief networks. We report superior performance on standard image classification benchmarks: Scene-15, Caltech-101, CIFAR10 and CIFAR10-ImageNet.

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Download slides icon Download slides: nips2010_bo_kdvr_01.pdf (331.8 KB)

Download article icon Download article: nips2010_0821.pdf (332.4 KB)


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