Dictionary learning for sparse representation has drawn considerable attention in recent years. In particular, the K-SVD algorithm is an efficient approach, and various modifications of the K-SVD have been developed for applications such as face recognition. However, the efficient storage of the dictionary has not been studied. Currently, the dictionary is simply normalized and saved as floating-point numbers, which could be quite large and lead to excessive cost and delay if the dictionary needs to be transmitted, e.g., to mobile users. In this paper, we develop a quantized K-SVD (Q-KSVD) to reduce the storage of the dictionary. We compress each basis image in the dictionary by the conventional image coding method. Moreover, we integrate the image compression step into various modified K-SVD optimization schemes, and develop an algorithm to find the optimal dictionary when there is a constraint on the total bits of the compressed dictionary. Our algorithm selects dictionary bases by ranking the contribution-rate slopes of all bases. This method also serves as an efficient approach to find the optimal number of bases of the dictionary at each rate constraint. Face recognition experiments using four K-SVD-based methods show that our method can achieve different tradeoffs between the dictionary storage space and the recognition accuracy. It can achieve comparable performance with as little as 3% of the original storage space. It can even yield higher accuracy than the uncompressed dictionary in some cases.
['Lei Liu', 'Jie Liang', 'Yao Zhao', 'Chunyu Lin', 'Huihui Bai']
Quantized dictionary for sparse representation