Presentation 2014-03-13
Neighbor-to-Neighbor Search for Fast Image Classification
Nakamasa INOUE, Koichi SHINODA,
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Abstract(in English) We propose Neighbor-to-Neighbor (NTN) search for fast image classification. The NTN search reduces computational cost of vector quantization (VQ) and Gaussian mixture models (GMMs) in image classification frame-works such as bag-of-visual-words and Fisher vector. The NTN search finds similar input vectors from a neighbor vector to a neighbor vector to skip some calculations based on the similarity of the input vectors. For example, in dense SIFT, the NTN search seeks similar descriptors from an adjacent descriptor to the other adjacent descriptors. We evaluated our method on the PASCAL VOC 2007 classification challenge task. The NTN search for VQ reduced the computational cost by 77.4%, and the NTN search for GMM reduced it by 89.3%, without any significant degradation in classification performance.
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Keyword(in English) Image Classification / Gaussian Mixture Models / Bag-of-visual-words
Paper # PRMU2013-184
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Conference Information
Committee PRMU
Conference Date 2014/3/6(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Neighbor-to-Neighbor Search for Fast Image Classification
Sub Title (in English)
Keyword(1) Image Classification
Keyword(2) Gaussian Mixture Models
Keyword(3) Bag-of-visual-words
1st Author's Name Nakamasa INOUE
1st Author's Affiliation Department of Computer Science, Tokyo Institute of Technology()
2nd Author's Name Koichi SHINODA
2nd Author's Affiliation Department of Computer Science, Tokyo Institute of Technology
Date 2014-03-13
Paper # PRMU2013-184
Volume (vol) vol.113
Number (no) 493
Page pp.pp.-
#Pages 6
Date of Issue