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

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #152

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.

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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