Application of evolutionary and swarm optimization in computer vision: a literature survey
- PDF / 2,855,046 Bytes
- 34 Pages / 595 x 791 pts Page_size
- 54 Downloads / 138 Views
IPSJ Transactions on Computer Vision and Applications
REVIEW PAPER
Open Access
Application of evolutionary and swarm optimization in computer vision: a literature survey Takumi Nakane1† , Naranchimeg Bold2† , Haitian Sun2† , Xuequan Lu3 , Takuya Akashi2 and Chao Zhang1*
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. Keywords: Evolutionary algorithms, Swarm algorithms, Computer vision, Literature survey
1 Introduction Many computer vision tasks can be regarded and formulated as a convex optimization, which allows a global optimum to be mathematically computed [110–112]. However, most of these tests can be highly non-convex and even ill-posed. As a result, there may exist numerous optima, with no solution, a non-unique solution, or an unstable solution, particularly under real-world settings that involve noisy or missing data. Regarding the non-convexity, for example, segmentation problems (Section 5) in computer vision can be cast as an energy minimization problem, which is applied to formulate an energy function over labels of pixels, such that the best solution can be obtained by minimizing the amount of energy. However, when the energy function given is complex, finding the exact energy minimum is NP-hard and the convex solvers are unable to explore the exponential number of local optima efficiently without adding *Correspondence: [email protected] † Takumi Nakane, Naranchimeg Bold and Haitian Sun contributed equally to this work. 1 Department of Engineering, University of Fukui, Fukui, Japan Full list of author information is available at the end of the article
additional constraints or hypotheses. Regarding the illposed problem, many tasks require optimizing the parameters of a certain mathematical model to reproduce the observations. For example, in face recognition problems (Section 9), there are various parameters that need to be tuned to model a “face likeness.” Depending on the amount and quality of the training samples, finding a parameter setting that can reproduce the training labels could be extremely difficult. By contrast, evolutionary algorithms (EAs) and swarm algorithms (SAs) are powerful metaheuristic tools used to search for solutions within a potentially huge solution space or provide approximate solutions for solving combinational constraints that may not hold stable solutions. T
Data Loading...
