Object categorization via sparse representation of local features
conference contribution
posted on 2012-01-01, 00:00authored byJin Wang, S Xiangping, Ronghua Chen, Fenghua She, Q Wang
Sparse representation has been introduced to address many recognition problems in computer vision. In this paper, we propose a new framework for object categorization based on sparse representation of local features. Unlike most of previous sparse coding based methods in object classification that only use sparse coding to extract high-level features, the proposed method incorporates sparse representation and classification into a unified framework. Therefore, it does not need a further classifier. Experimental results show that the proposed method achieved better or comparable accuracy than the well known bag-of-features representation with various classifiers.
History
Event
International Conference on Pattern Recognition (21st : 2012 : Tsukuba, Japan)
Pagination
3005 - 3008
Publisher
IEEE
Location
Tsukuba, Japan
Place of publication
[Tsukuba, Japan]
Start date
2012-11-11
End date
2012-11-15
ISSN
1051-4651
ISBN-13
9784990644109
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2012, ICPR
Title of proceedings
ICPR 2012 : Proceedings of the 21st International Conference on Pattern Recognition