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AUSAI
2011
Springer

Image Feature Selection Based on Ant Colony Optimization

7 years 6 months ago
Image Feature Selection Based on Ant Colony Optimization
Image feature selection (FS) is an important task which can affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm based on ant colony optimization (ACO). For n features, most ACO-based feature selection methods use a complete graph with O(n2 ) edges. However, the artificial ants in the proposed algorithm traverse on a digraph with only 2n arcs. The algorithm adopts classifier performance and the number of the selected features as heuristic information, and selects the optimal feature subset in terms of feature set size and classification performance. Experimental results on various images show that our algorithm can obtain better classification accuracy with a smaller feature set comparing to other algorithms.
Ling Chen, Bolun Chen, Yixin Chen
Added 12 Dec 2011
Updated 12 Dec 2011
Type Journal
Year 2011
Where AUSAI
Authors Ling Chen, Bolun Chen, Yixin Chen
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