TY - GEN
T1 - Image feature selection based on ant colony optimization
AU - Chen, Ling
AU - Chen, Bolun
AU - Chen, Yixin
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - ant colony optimization
KW - dimensionality reduction
KW - feature selection
KW - image classification
UR - https://www.scopus.com/pages/publications/83755173692
U2 - 10.1007/978-3-642-25832-9_59
DO - 10.1007/978-3-642-25832-9_59
M3 - Conference contribution
AN - SCOPUS:83755173692
SN - 9783642258312
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 580
EP - 589
BT - AI 2011
T2 - 24th Australasian Joint Conference on Artificial Intelligence, AI 2011
Y2 - 5 December 2011 through 8 December 2011
ER -