Image Change Detection Using Particle Swarm Optimization

Abstract

Image change detection can be expressed as a function of time period, whose main objective is to find the changes on the same area at different time intervals, which is a complex and intractable one. Due to large search space, general optimization algorithm fails to give the solution in a promising amount of time. So particle swarm optimization (PSO), one of the swarm-based approaches, can be used as an efficient tool, which the authors have explored in this paper. This mechanism aims to find a change mask that performs partitioning of image into changed and unchanged areas so that the weighted sum of mean square errors of both areas is minimized. This leads to accurate change detection with less noise in a feasible time period.

Spectrum Allocation in Cognitive Radio: A PSO-based Approach

“…The involvement of local best and global best positions ensures the coordination and exchange of information among particles and also makes sure that the particles are directed closer to the solution with each step. The particles eventually converge to the solution by repeating these steps till certain stopping criteria are reached [25,26].…”

Acceleration harmonic estimation for a hydraulic shaking table by using particle swarm optimization

“…Mirhassani et al (2015) used PSO to develop an improved maximum power point tracking strategy for photovoltaic systems. Sagnika et al (2015) presented an image change detection technique based on PSO. Sedighizadeh and Kashani (2015) made use of Tribe-PSO algorithm, multi-layered and multi-phased of hybrid PSO model to identify parameters of proton exchange membrane fuel cell model.…”