Earticle

초록

영어

Probabilistic latent semantic analysis (pLSA) has been widely used by researchers for human action recognition from video sequences. However, one of the major disadvantages of pLSA and its other extensions is that category labels of training samples are not fully used in model learning procedure for classification task. In this paper, a supervised pLSA (spLSA) model is proposed for overcoming this drawback. By adding an observable category variable to generative process of classic pLSA, spLSA is endowed with more discriminative power. Thus, this model provides a unified framework for semantic analysis and object classification, where the topics formulation is guided by spLSA towards more discriminative and the mapping between the topics and the action categories are described in a fully probabilistic manner. Experimental results show that spLSA substantially outperforms pLSA and achieves comparable or better performances than latent dirichlet allocation based supervised models and other state-of-the-art methods.

목차

Abstract
 1. Introduction
 2. Supervised pLSA
  2.1. Classic pLSA
  2.2. Supervised pLSA
 3. Model Fitting and Classification
  3.1. Model Fitting
  3.2. Classification
 4. Experiments and Results
  4.1. Datasets
  4.2. Experimental Setup
  4.3. Comparison with other Topic Models
  4.4. Comparison with state-of-the-art Methods
  4.5. Discussion
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Tingwei Wang [ School of Computer Science and Engineering, Nanjing University of Science and Technology, SM, University of Jinan ]
• Chuancai Liu [ School of Computer Science and Engineering, Nanjing University of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605