Earticle

초록

영어

In this paper, we propose a novel and effective image model—Image Latent Semantic Analysis (ILSA) for extracting latent semantic features of face image, and recognizing face with Support Vector Machine (SVM). The novel feature extraction by the ILSA model can be better overcome the impact of some negative factors, such as the image quality fuzzy, illumination changes effect. The main contribution of the paper is that the ILSA features can obtain a wealth of information than the conventional image semantic features and has a stronger expression and classification abilities than the low-level features. The experimental results on the ORL and large-scale FERET databases show that the proposed algorithm significantly outperforms other well-known algorithms.

목차

Abstract
 1. Introduction
 2. Related Theory
  2.1. Latent Semantic Analysis
  2.2. Support Vector Machine
 3. Proposed Method: Image Latent Semantic Analysis
 4. Experiments and Analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Jucheng Yang [ Ahead Software Company Limited, College of Computer Science and Information Engineering, Tianjin University of Science and Technology ]
• Min Luo [ Jiangxi Institute of Computing Technology ]
• Yanbin Jiao [ School of Information Technology, Jiangxi University of Finance and Economics ]

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