HPC MSU

Publication Abstract

Optimizing Maximally Stable Extremal Region Parameters Using Machine Learning

Davis, J., Bednar, A., Goodin, C. T., Durst, P., Anderson, D., & Bethel, C. L. (2017). Optimizing Maximally Stable Extremal Region Parameters Using Machine Learning. SPIE Defense + Commercial Sensing Expo - Infrared Technology and Applications XLIII Track. Anaheim, CA: SPIE.

Abstract

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems that are considered intractable. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing the five MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several modifications and additions to the GA and PSO are required to successfully detect stop signs in simulated images. These modifications include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an \'\'execution best\'\' omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and modifications required to generate successful problem specific parameter sets.