Learning a context aware dictionary for sparse representation

Recent successes in the use of sparse coding for many computer vision applications have triggered the attention towards the problem of how an over-complete dictionary should be learned from data. This is because the quality of a dictionary greatly affects performance in many respects, including computational. While so far the focus has been on learning compact, reconstructive, and discriminative dictionaries, in this work we propose to retain the previous qualities, and further enhance them by learning a dictionary that is able to predict the contextual information surrounding a sparsely coded signal. The proposed framework leverages the K-SVD for learning, fully inheriting its benefits of simplicity and efficiency. A model of structured prediction is designed around this approach, which leverages contextual information to improve the combined recognition and localization of multiple objects from multiple classes within one image. Results on the PASCAL VOC 2007 dataset are in line with the state-of-the-art, and clearly indicate that this is a viable approach for learning a context aware dictionary for sparse representation.