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Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

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Listed:
  • Xiang Zhang
  • Naiyang Guan
  • Dacheng Tao
  • Xiaogang Qiu
  • Zhigang Luo

Abstract

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

Suggested Citation

  • Xiang Zhang & Naiyang Guan & Dacheng Tao & Xiaogang Qiu & Zhigang Luo, 2015. "Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.
  • Handle: RePEc:plo:pone00:0124685
    DOI: 10.1371/journal.pone.0124685
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Naiyang Guan & Lei Wei & Zhigang Luo & Dacheng Tao, 2013. "Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    3. Dongxiao He & Di Jin & Carlos Baquero & Dayou Liu, 2014. "Link Community Detection Using Generative Model and Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    4. Jonas De Vylder & Jan Aelterman & Trees Lepez & Mado Vandewoestyne & Koen Douterloigne & Dieter Deforce & Wilfried Philips, 2013. "A Novel Dictionary Based Computer Vision Method for the Detection of Cell Nuclei," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-9, January.
    5. Naiyang Guan & Xiang Zhang & Zhigang Luo & Dacheng Tao & Xuejun Yang, 2013. "Discriminant Projective Non-Negative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
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