Analysis sparse coding models for image-based classification

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.