Abstract
Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in solving combinatorial and NP-hard optimization problems in various research fields. However, in the field of computer vision, related surveys have not been updated during the last decade. In this study, inspired by the recent development of deep neural networks in computer vision, which embed large-scale optimization problems, we first describe a literature survey conducted to compensate for the lack of relevant research in this area. Specifically, applications related to the genetic algorithm and differential evolution from EAs, as well as particle swarm optimization and ant colony optimization from SAs and their variants, are mainly considered in this survey.
Highlights
Many computer vision tasks can be regarded and formulated as a convex optimization, which allows a global optimum to be mathematically computed [110,111,112]
We systematically summarize the studies in which four selected algorithms are involved with respect to different computer vision tasks: a neural network (Section 4), image segmentation (Section 5), feature detection and selection (Section 6), image matching (Section 7), visual tracking (Section 8), face recognition (Section 9), human action recognition (Section 10), and a few other studies (Section 11)
The results demonstrate that, instead of an exhaustive search over all features, an evolutionary search can speed up the training and effectively find good features in a large feature pool within a reasonable time
Summary
Many computer vision tasks can be regarded and formulated as a convex optimization, which allows a global optimum to be mathematically computed [110,111,112]. Most of these tests can be highly non-convex and even ill-posed. To avoid being trapped in the local optima and provide a satisfactory solution, EAs and SAs have been successfully adopted to solve various computer vision tasks, which are listed and classified in this survey
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