Firefly Algorithm and Its Variants in Digital Image Processing: A Comprehensive Review
The significance and requirements of digital image processing arise from two main areas of applications: the improvement of visual information for human interpretation and the encoding of scene data for the independent perception of machines. However, human is often involved in such processing for manually tuning up the parameters, which takes a long time, and it remains as an unresolved issue. Nature is a brilliant and enormous source of inspiration for resolving difficult and complicated problems in computer science, as it possesses incredibly diverse, vibrant, flexible, complicated, and intriguing phenomena. In practice, selecting the optimum parameters for any technique is an optimization problem. Nature inspired algorithms are metaheuristics that imitate the works of nature to solve optimization issues, leading to a new era in computing. There are several dozens of classical metaheuristic optimization algorithms reported in the literature, such as genetic algorithm, ant colony optimization, and particle swarm optimization. Though due to the efficacy and success in solving various digital image analysis problems, the firefly algorithm which is also a metaheuristic algorithm, inspired by fireflies' flashing behaviour in nature, is used in various image analysis optimization studies. This work is dedicated to a comprehensive review of the firefly algorithm to solve optimization problems in various steps of digital image analysis, like image preprocessing, segmentation, compression, feature selection, and classification. Various applications of the firefly algorithm in image analysis are also discussed in this review. Key issues and future research directions will also be highlighted.
Springer Tracts in Nature Inspired Computing