Hybrid bio-inspired lateral inhibition and Imperialist Competitive Algorithm for complicated image matching

Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

“…This mechanism, which provides great help to detect object in cluttered background, can enhance the contrast of sensory information and reduce low-frequency noises [10]. Experimental results confirm that SFS is more capable than several intelligent algorithms such as IFABC [11], BEABC [12], SMS [13], and ICA [7] in this LI-based template matching scheme. The rest of the paper is structured as follows.…”

“…Before performing the second procedure, all points attained from the first update process are ranked according to (5). In the same manner as the first statistical procedure, if < for a new point , the present position of is changed according to (7) and (8) as follows; otherwise there is no update.…”

“…Some parameters of LI model are set following the similar works reported in [7,9,10]. In detail, = = 2 and the matrix that contains lateral inhibition coefficients , are set as follows: .…”

“…The existing template matching techniques can be divided into two categories: the intensity-based approach and the feature-based approach [7]. The intensity-based method can be regarded as an optimization process of finding the maximum similar degree between the template and the original image.…”

“…In recent years, researchers have gradually shifted their interests towards the utilization of intelligent algorithms, such as artificial bee colony (ABC) [10], internal-feedback artificial bee colony (IFABC) [11], balance-evolution artificial bee colony (BEABC) [12], states of matter search (SMS) algorithm [13], and imperialist competitive algorithm (ICA) [7] which have been proposed for this template matching problem. Although these algorithms aim to reduce the computation load of the global optimums searching, none of them can completely guarantee the derivation of suboptimal matching results.…”