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A Novel \(\ell ^1\)-graph Based Image Classification Algorithm

  • Jia-Yue XuEmail author
  • Shu-Tao Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

In original sparse representation based classification algorithms, each training sample belongs to exactly one class, neglecting the association between the training sample and the other classes. However, different classes’ features are visually similar and correlated (e.g. facial images), which means the association between the training sample and the different classes contain important information, and must be taken into consideration. In this paper, we propose a novel \(\ell ^1\)-graph based image classification algorithm (LGC). Our algorithm can automatically calculate associations between training samples and all classes, which are used for future classification. We evaluate our method on some popular visual benchmarks, the experimental results prove the effectiveness of our method.

Keywords

Image classification Sparse representation \(\ell ^1\)-graph 

Notes

Acknowledgments

This research is supported in part by the Major State Basic Research Development Program of China (973 Program, 2012CB315803), the National Natural Science Foundation of China (61371078), and the Research Fund for the Doctoral Program of Higher Education of China (20130002110051).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School at ShenzhenTsinghua UniversityShenzhenPeople’s Republic of China

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