A new approach for template matching in digital images using an Artificial Bee Colony Algorithm

Abstract

In this paper, we applied the Artificial Bee Colony Algorithm (ABC) to the object recognition in the images. ABC is a new metaheuristics approach inspired by the collective and individual foraging behavior of honey bee swarm. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene, considering that it may be translated, scaled, rotated and/or partially occluded. This will result in location of the given reference image in the target landscape image. Results of the experiments with grayscale and color images show that the ABC is faster than other evolutionary algorithms and with comparable accuracy.

Geometry based hybrid method for determining lesion area

“…Ma et al [12] studied to enhance threshold optimal value of grayscale color between pixels. Application of ABC for recognition of an object within certain images was introduced in [13]. The objective of their work was to find a pattern or template of an object anywhere on a target scene.…”

Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

“…The swarm updating in ABC is due to two processes namely, the variation process and the selection process which are responsible for exploration and exploitation, respectively. The ABC algorithm has successfully been tested on almost all domains of science and engineering like electronics engineering (Chidambaram and Lopes 2009;Kavian et al 2012), electrical engineering (Jones and Bouffet 2008;Nayak et al 2009;Sulaiman et al 2012), computer science engineering (Karaboga and Cetinkaya 2011;Lam et al 2012;Lei et al 2010), mechanical engineering (Banharnsakun et al 2012;Pawar et al 2008;Xu and Duan 2010), civil engineering (Akay and Karaboga 2012;Li et al 2011;Mandal et al 2012), medical pattern classification and clustering problems (Karaboga et al 2008) and mathematical graph problems (Xing et al 2007;Singh 2009;Yeh and Hsieh 2011). Many of the recent modifications and applications of ABC algorithm can be studied in Bansal et al (2013b).…”

A review on Artificial Bee Colony algorithm

“…In recent years, ABC is used for a variety of problems such as constrained optimization [9], in image processing [10], in data mining, in engineering design [11] and many others. Since it was developed, many modifications to the original have been developed according to applications across a wide range of domains.…”