Efficient ant colony optimization for image feature selection

Authors:
Bolun Chen;Ling Chen;Yixin Chen
Affiliations:
College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Department of Computer Science, Yangzhou University, Yangzhou, China and State Key Lab of Novel Software Tech, Nanjing University, Nanjing, China;Department of Computer Science and Engineering, Washington University in St Louis, USA
Venue:
Signal Processing
Year:
2013

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Abstract

Feature selection (FS) is an important task which can significantly 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, existing ACO-based feature selection methods need to traverse a complete graph with O(n^2) edges. However, we propose a novel algorithm in which the artificial ants traverse on a directed graph with only O(2n) arcs. The algorithm incorporates the classification performance and feature set size into the heuristic guidance, and selects a feature set with small size and high classification accuracy. We perform extensive experiments on two large image databases and 15 non-image datasets to show that our proposed algorithm can obtain higher processing speed as well as better classification accuracy using a smaller feature set than other existing methods.