Abstract
Visual shape matching is a critical topic in pattern recognition applications. Atomic potential matching (APM) model is a relatively new shape matching methodology inspired by potential field attractions. Compared to the conventional edge potential function model, APM not only encourages the right matching parts through attraction, but also repels the wrong matching parts. This feature enables APM to cope with targets that hide in the intricate background. This study comprehensively investigates the convergence performances of various state-of-the-art artificial bee colony (ABC) algorithms in shape matching problems on the basis of APM framework. Repeated simulations are conducted to evaluate the optimization abilities of the concerned ABC variants and experimental results indicate that the prevailing remedies for the conventional ABC algorithm, especially efforts made in the local exploitation phase, are not efficacious to promote optimization capability. Explanations regarding the comparative results are provided as well.
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