A VIDEO TENSOR SELF-DESCRIPTOR BASED ON VARIABLE SIZE BLOCK MATCHING

Authors

  • HELENA ALMEIDA MAIA Department of Computer Science, Universidade Federal de Juiz de Fora Juiz de Fora, Minas Gerais, Brazil
  • ANA MARA DE OLIVEIRA FIGUEIREDO Department of Computer Science, Universidade Federal de Juiz de Fora Juiz de Fora, Minas Gerais, Brazil
  • FABIO LUIZ MARINHO DE OLIVEIRA Department of Computer Science, Universidade Federal de Juiz de Fora Juiz de Fora, Minas Gerais, Brazil
  • VIRGINIA FERNANDES MOTA Department of Computer Science, Universidade Federal de Minas Gerais Belo Horizonte, Minas Gerais, Brazil
  • MARCELO BERNARDES VIEIRA Department of Computer Science, Universidade Federal de Juiz de Fora Juiz de Fora, Minas Gerais, Brazil

Keywords:

self-descriptor, compact descriptor, variable size block matching, human ac- tion recognition

Abstract

This paper presents a dierent and simple approach for video description using only block matching vectors, considering that most works on the eld are based on the gradient of image intensities. We rst divide the image into blocks of dierent sizes. The block matching method returns a displacement vector for each block, which we use as motion information to obtain orientation tensors and to generate the nal self-descriptor, since it depends only on the input video. The resulting descriptor is evaluated by a classication of KTH, UCF11 and Hollywood2 video datasets with a non-linear SVM classier. Our results indicate that the method runs fast and has fairly competitive results compared to similar approaches. It is suitable when the time response is a major application issue. It also generates compact descriptors which are desirable to describe large datasets.

 

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Published

2015-08-30

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