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The Scientific World Journal
Volume 2014 (2014), Article ID 906861, 14 pages
Research Article

A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching

1School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
2School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
3School of Mathematics and Systems Science & LMIB, Beihang University, Beijing 100191, China

Received 24 December 2013; Accepted 26 March 2014; Published 29 April 2014

Academic Editors: P. Melin, D. Simson, C.-W. Tsai, and F. Yu

Copyright © 2014 Bai Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-known normalized cross correlation model as a similarity criterion. The searching procedure for the best-match location is carried out through an internal-feedback artificial bee colony (IF-ABC) algorithm. IF-ABC algorithm is highlighted by its effort to fight against premature convergence. This purpose is achieved through discarding the conventional roulette selection procedure in the ABC algorithm so as to provide each employed bee an equal chance to be followed by the onlooker bees in the local search phase. Besides that, we also suggest efficiently utilizing the internal convergence states as feedback guidance for searching intensity in the subsequent cycles of iteration. We have investigated four ideal template matching cases as well as four actual cases using different searching algorithms. Our simulation results show that the IF-ABC algorithm is more effective and robust for this template matching mission than the conventional ABC and two state-of-the-art modified ABC algorithms do.