Summary

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 (Siyahjani & Doretto, 2012) all the previous qualities are retained and are further enhanced by learning a dictionary that is able to predict the contextual information surrounding a sparsely coded signal.

References

  1. ACCV
    Learning a Context Aware Dictionary for Sparse Representation Siyahjani, F., and Doretto, G. In Proceedings of The Asian Conference on Computer Vision, 2012. Oral abstract bibTeX pdf