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Representative Video Action Discovery Using Interactive Non-negative Matrix Factorization

  • Hui Teng
  • Huaping Liu
  • Lianzhi Yu
  • Fuchun Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

In this paper, we develop an interactive Non-negative Matrix Factorization method for representative action video discovery. The original video is first evenly segmented into some short clips and the bag-of-words model is used to describe each clip. Then a temporal consistent Non-negative Matrix Factorization model is used for clustering and action segmentation. Since the clustering and segmentation results may not satisfy the user’s intention, two extra human operations: MERGE and ADD are developed to permit user to improve the results. The newly developed interactive Non-negative Matrix Factorization method can therefore generate personalized results. Experimental results on the public Weizman dataset demonstrate that our approach is able to improve the action discovery and segmentation results.

Keywords

Interactive action summarization Non-negative Matrix Factorization video analysis 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Hui Teng
    • 1
    • 2
    • 3
  • Huaping Liu
    • 2
    • 3
  • Lianzhi Yu
    • 1
  • Fuchun Sun
    • 2
    • 3
  1. 1.School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.State Key Lab. of Intelligent Technology and SystemsBeijingChina

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