Activity Recognition in Still Images with Transductive Non-negative Matrix Factorization

  • Naiyang GuanEmail author
  • Dacheng Tao
  • Long Lan
  • Zhigang Luo
  • Xuejun Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)


Still image based activity recognition is a challenging problem due to changes in appearance of persons, articulation in poses, cluttered backgrounds, and absence of temporal features. In this paper, we proposed a novel method to recognize activities from still images based on transductive non-negative matrix factorization (TNMF). TNMF clusters the visual descriptors of each human action in the training images into fixed number of groups meanwhile learns to represent the visual descriptor of test image on the concatenated bases. Since TNMF learns these bases on both training images and test image simultaneously, it learns a more discriminative representation than standard NMF based methods. We developed a multiplicative update rule to solve TNMF and proved its convergence. Experimental results on both laboratory and real-world datasets demonstrate that TNMF consistently outperforms NMF.


Still image based action recognition Non-negative matrix factorization Transductive learning 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Naiyang Guan
    • 1
    Email author
  • Dacheng Tao
    • 2
  • Long Lan
    • 1
  • Zhigang Luo
    • 1
  • Xuejun Yang
    • 3
  1. 1.Science and Technology on Parallel and Distributed Processing LaboratoryCollege of Computer, National University of Defense TechnologyChangshaPeople’s Republic of China
  2. 2.Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  3. 3.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaPeople’s Republic of China

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