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Content Provider | IEEE Xplore Digital Library |
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Author | Jianchao Yang Kai Yu Yihong Gong Huang, T. |
Copyright Year | 2009 |
Description | Author affiliation: Beckman Inst., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Jianchao Yang; Huang, T.) || NEC Labs. America, Cupertino, CA, USA (Kai Yu; Yihong Gong) |
Abstract | Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity $O(n^{2}$ ~ $n^{3})$ in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algorithms to handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors. |
Starting Page | 1794 |
Ending Page | 1801 |
File Size | 564840 |
Page Count | 8 |
File Format | |
ISBN | 9781424439928 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2009.5206757 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-06-20 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Image coding Image classification Scanning probe microscopy Kernel Histograms Testing Image representation Vector quantization Image segmentation Computational complexity |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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