Kernel Descriptors for Visual Recognition
published: March 25, 2011, recorded: December 2010, views: 7181
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Description
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: nips2010_bo_kdvr_01.pdf (331.8 KB)
Download article: nips2010_0821.pdf (332.4 KB)
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