An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching

Abstract

In this paper, the authors present an improved Artificial Bee Colony Algorithm (ABC) for the object recognition problem in complex digital images. The ABC is a new metaheuristics approach inspired by the collective foraging behavior of honey bee swarms. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene that may contain noise and changes in brightness and contrast. First, several search strategies were tested to find the most appropriate. Next, many experiments were done using complex digital grayscale and color images. Results are analyzed and compared with other algorithms through Pareto plots and graphs that show that the improved ABC was more efficient than the original ABC.

A comparison of swarm intelligence algorithms for structural engineering optimization

“…Some applications found in the literature using the ABC algorithm include the generalized assignment problem optimization , energy distribution network configuration , neural network training , data clustering , solving integer programming benchmarks , template matching in digital images , clustering , and signal model parameter extraction .…”

“…Similar to the PSO, a variant of the original algorithm including an explosion (mass extinction) procedure was also implemented. This simple procedure has been shown more effective than the original ABC . As in the PSO‐X, in the ABC with explosion (ABC‐X), when stagnation of the best solution for a certain number of iterations ( it x ) is observed, the explosion takes place.…”

“…Some applications found in the literature using the ABC algorithm include the generalized assignment problem optimization [19], energy distribution network configuration [20], neural network training [21], data clustering [22], solving integer programming benchmarks [23], template matching in digital images [24], clustering [25], and signal model parameter extraction [26].Similar to the PSO, a variant of the original algorithm including an explosion (mass extinction) procedure was also implemented. This simple procedure has been shown more effective than the original ABC [24]. As in the PSO-X, in the ABC with explosion (ABC-X), when stagnation of the ¶ ABC Repository…”

A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session

“…In addition, the framework of ABC is relatively simple and clear, making it easy to acquire satisfactory results at a low computational cost. Such merits have given rise to applications of ABC spanning across various areas, such as trajectory planning [5][6][7], structure optimization [8][9][10], clustering [11], machine learning [12], scheduling [13][14][15][16], image recognition [17][18][19][20] etc. Benchmark Functions of CEC 2014 Special Session Regarding the modifications ever made for the conventional ABC, from the author's viewpoint, the prevailing ways can be broadly classified into three categories.…”