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Learning contrastive feature distribution model for interaction recognition.
- Source :
-
Journal of Visual Communication & Image Representation . Nov2015, Vol. 33, p340-349. 10p. - Publication Year :
- 2015
-
Abstract
- In this paper, we learn a Contrastive Feature Distribution Model (CFDM) for interaction recognition. Our contributions are three-folded. First of all, we introduce an intra–inter-frame skeleton feature for interaction description. Secondly, we learn CFDM for a discriminative representation of interactions. In this step, we mine contrastive features to create a dictionary, and learn the probability distribution of dictionary words to construct CFDM in positive and negative training samples. With CFDM, we represent interactions in a discriminative way for recognition. Since there is few skeleton based interaction databases now, we capture a new database, CR-UESTC, which is the third contribution. We evaluate the proposed CFDM approach on CR-UESTC and SBU interaction databases, and compare the result of CFDM with the CM and the BoW approach. The comparison indicates that the recognition accuracy of three approaches is: CFDM > CM > BoW. Compared with Yun et al. (2012), the proposed CFDM also obtain a better result on SBU database. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 33
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 110791155
- Full Text :
- https://doi.org/10.1016/j.jvcir.2015.10.001