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Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Yaqing WANG · Quanming Yao · James Kwok · Lionel NI

Wed Jul 11 05:20 AM -- 05:30 AM (PDT) @ A5
in Sparsity and Compressed Sensing 1 »

Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing. However, existing methods have limited scalability. In this paper, instead of convolving with a dictionary shared by all samples, we propose the use of a sample-dependent dictionary in which each filter is a linear combination of a small set of base filters learned from data. This added flexibility allows a large number of sample-dependent patterns to be captured, which is especially useful in the handling of large or high-dimensional data sets. Computationally, the resultant model can be efficiently learned by online learning. Extensive experimental results on a number of data sets show that the proposed method outperforms existing CSC algorithms with significantly reduced time and space complexities.

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Author Information

Yaqing WANG (Hong Kong University of Science and Technology)
Quanming Yao (4Paradigm)
James Kwok (Hong Kong University of Science and Technology)
Lionel NI (University of Macau)

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