Image feature selection based on ant colony optimization

Authors:
Ling Chen;Bolun Chen;Yixin Chen
Affiliations:
Department of Computer Science, Yangzhou University, Yangzhou, China;Department of Computer Science, Yangzhou University, Yangzhou, China;Department of Computer Science, Washington University in St Louis
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Abstract

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(n 2) edges. However, the artificial ants in the proposed algorithm traverse on a directed graph 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.