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Robust Multi-view Common Component Learning

  • Jiamiao Xu
  • Xinge You
  • Shi Yin
  • Peng Zhang
  • Wei Yuan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 773)

Abstract

In many computer vision applications, one object usually exists more than one data representation. This paper focuses on the specific problem of cross-view recognition, which aims to recognize objects from different views. A majority of representative works mainly attempt to seek a common subspace, in which the Euclidean distance of within-class data is short. Intuitively, the recognition performance will be better if the various data from the same object have completely same representation in the common space. Therefore, this paper proposes robust multi-view common component learning (RMCCL) algorithm, which learns multiple linear transforms to extract the common component of multi-view data from the same instance. To enhance the discriminant ability and robustness of subspace, we introduce binary label matrix technology and serve Cauchy loss as our error measurement. RMCCL can be decomposed into two subproblems by Alternating optimization method, and each subproblem can be optimized by Iteratively Reweight Residuals (IRR) technique. Extensive experiments in both two-view and multi-view datasets demonstrate that the our method outperforms other state-of-the-art approaches.

Keywords

Cross-view recognition Multi-view learning Common space 

Notes

Acknowledgment

This work was supported partially by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2015BAK36B00), in part by the Key Science and Technology of Shen zhen (No. CXZZ20150814155434903), in part by the Key Program for International S&T Cooperation Projects of China (No. 2016YFE0121200), in part by the National Natural Science Foundation of China (No. 61571205), in part by the National Natural Science Foundation of China (No. 61772220).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Jiamiao Xu
    • 1
  • Xinge You
    • 1
  • Shi Yin
    • 1
  • Peng Zhang
    • 1
  • Wei Yuan
    • 1
  1. 1.School of Electronics and Information EngineeringHuazhong University of Science and TechnologyWuhanChina

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