Efficient dictionary learning for visual categorization

Tang, Jun, Shao, Ling and Li, Xuelong (2014) Efficient dictionary learning for visual categorization. Computer Vision and Image Understanding, 124. pp. 91-98. ISSN 1077-3142

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We propose an efficient method to learn a compact and discriminative dictionary for visual categorization, in which the dictionary learning is formulated as a problem of graph partition. Firstly, an approximate kNN graph is efficiently computed on the data set using a divide-and-conquer strategy. And then the dictionary learning is achieved by seeking a graph topology on the resulting kNN graph that maximizes a submodular objective function. Due to the property of diminishing return and monotonicity of the defined objective function, it can be solved by means of a fast greedy-based optimization. By combing these two efficient ingredients, we finally obtain a genuinely fast algorithm for dictionary learning, which is promising for large-scale datasets. Experimental results demonstrate its encouraging performance over several recently proposed dictionary learning methods.

Item Type: Article
Uncontrolled Keywords: visual categorization,efficient dictionary learning,submodular optimization,fast graph construction
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 04 Feb 2017 03:27
Last Modified: 30 Sep 2021 08:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/62303
DOI: 10.1016/j.cviu.2014.02.007

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