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Locality discriminative coding for image classification

Published: 17 August 2013 Publication History

Abstract

The Bag-of-Words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. Extensive experimental results on 4 challenging benchmark datasets show that our LDC method outperforms 6 state-of-the-art image classification methods (3 based on NBNN, 3 based on BOW).

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ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
August 2013
419 pages
ISBN:9781450322522
DOI:10.1145/2499788
  • Conference Chair:
  • Tat-Seng Chua,
  • General Chairs:
  • Ke Lu,
  • Tao Mei,
  • Xindong Wu
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • NSF of China: National Natural Science Foundation of China
  • University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
  • Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2013

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Author Tags

  1. bag-of-words
  2. discriminative
  3. feature coding

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  • Research-article

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ICIMCS '13
Sponsor:
  • NSF of China
  • University of Sciences & Technology, Hefei

Acceptance Rates

ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;
Overall Acceptance Rate 163 of 456 submissions, 36%

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  • (2019)Textile fabric defect detection based on low-rank representationMultimedia Tools and Applications10.1007/s11042-017-5263-z78:1(99-124)Online publication date: 1-Jan-2019
  • (2018)A new discriminative coding method for image classificationMultimedia Systems10.1007/s00530-014-0376-y21:2(133-145)Online publication date: 27-Dec-2018
  • (2016)How important is location information in saliency detection of natural imagesMultimedia Tools and Applications10.1007/s11042-015-2875-z75:5(2543-2564)Online publication date: 1-Mar-2016
  • (2014)How Important is Location in Saliency DetectionProceedings of International Conference on Internet Multimedia Computing and Service10.1145/2632856.2632945(10-13)Online publication date: 10-Jul-2014

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