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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Official URL: http://dx.doi.org/10.1016/j.cviu.2014.02.007


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
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 10:23
Last Modified: 12 Oct 2019 22:30
URI: http://nrl.northumbria.ac.uk/id/eprint/22813

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