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Non-negative Locality-Constrained Linear Coding for Image Classification

  • GuoJun LiuEmail author
  • Yang Liu
  • MaoZu Guo
  • PeiNa Liu
  • ChunYu Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9242)

Abstract

The most important issue of image classification algorithm based on feature extraction is how to efficiently encode features. Locality-constrained linear coding (LLC) has achieved the state of the art performance on several benchmarks, due to its underlying properties of better construction and local smooth sparsity. However, the negative code may make LLC more unstable. In this paper, a novel coding scheme is proposed by adding an extra non-negative constraint based on LLC. Generally, the new model can be solved by iterative optimization methods. Moreover, to reduce the encoding time, an approximated method called NNLLC is proposed, more importantly, its computational complexity is similar to LLC. On several widely used image datasets, compared with LLC, the experimental results demonstrate that NNLLC not only can improve the classification accuracy by about 1–4 percent, but also can run as fast as LLC.

Keywords

Locality-constrained Linear Coding (LLC) Non-negative constraint Spatial Pyramid Matching (SPM) Image classification 

Notes

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. HIT.NSRIF.2014069), Heilongjiang Province Science Foundation for Youths (Grant No. QC2014C071), National Natural Science Foundation of China (Grant No. 61173087, 61171185, 61271346), and Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20112302110040).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • GuoJun Liu
    • 1
    Email author
  • Yang Liu
    • 1
  • MaoZu Guo
    • 1
  • PeiNa Liu
    • 1
  • ChunYu Wang
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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