@inproceedings{eb587e4ab37e42cc81201b82d0d301f0,
title = "Analysis sparse coding models for image-based classification",
abstract = "Data-driven sparse models have been shown to give superior performance for image classification tasks. Most of these works depend on learning a synthesis dictionary and the corresponding sparse code for recognition. However in recent years, an alternate analysis coding based framework (also known as co-sparse model) has been proposed for learning sparse models. In this paper, we study this framework for image classification. We demonstrate that the proposed approach is robust and efficient, while giving a comparable or better recognition performance than the traditional synthesis-based models.",
keywords = "analysis sparse coding models, efficient sparse coding, image classification",
author = "Sumit Shekhar and Rama Chellappa",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",
year = "2014",
month = jan,
day = "28",
doi = "10.1109/ICIP.2014.7026054",
language = "American English",
series = "2014 IEEE International Conference on Image Processing, ICIP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5207--5211",
booktitle = "2014 IEEE International Conference on Image Processing, ICIP 2014",
address = "United States",
}