An improved particle swarm optimisation for video image segmentation

Video image segmentation is the key step of objects classification and recognition. Threshold-based image segmentation algorithms, especially those based on the two-dimensional maximum entropy, have been studied by many scholars now. Although there are many algorithms that can get the entropy, they have their own weaknesses. For instances, the exhaustive search algorithm is time-consuming to implement the real-time image segmentation, the convergence rate of the traditional genetic algorithm is relatively slow, the standard particle swarm algorithm is prone to premature and difficult to get the optimal solution in the high-dimensional data space. In this paper, we proposed an improved particle swarm optimisation algorithm based on the law of universal gravitation to obtain the optimal entropy. Experimental results show that the improved algorithm is robust and obtain fast convergence rate.

[1]  Chunxia Xiao,et al.  Fast level set image and video segmentation using new evolution indicator operators , 2012, The Visual Computer.

[2]  Amitava Chatterjee,et al.  A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding , 2008, Expert Syst. Appl..

[3]  Xiujie Qu,et al.  Novel detection method for infrared small targets using weighted information entropy , 2012 .

[4]  Xiaowei Zhou,et al.  Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Hui Wang,et al.  Diversity enhanced particle swarm optimization with neighborhood search , 2013, Inf. Sci..

[6]  Swagatam Das,et al.  Parameter selection of a Particle Swarm Optimisation dynamics by closed loop stability analysis , 2010, Int. J. Comput. Sci. Math..

[7]  Lin Wang,et al.  Multidimensional particle swarm optimization-based unsupervised planar segmentation algorithm of unorganized point clouds , 2012, Pattern Recognit..

[8]  Andrey A. Boguslavsky,et al.  The realtime Vision System for small-sized target tracking , 2007, Int. J. Comput. Sci. Math..

[9]  Mary M. Conte,et al.  Local image statistics: maximum-entropy constructions and perceptual salience. , 2012, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Sadoghi Yazdi Hadi,et al.  Gravitation based classification , 2013 .

[11]  Qiang Xu,et al.  A novel classification learning framework based on estimation of distribution algorithms , 2012, Int. J. Comput. Sci. Math..

[12]  Siba K. Udgata,et al.  Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems , 2012, Int. J. Bio Inspired Comput..

[13]  Nikolaos Papanikolopoulos,et al.  Robot Formations Using a Single Camera and Entropy-based Segmentation , 2012, J. Intell. Robotic Syst..

[14]  Charles T Ridgely.  Gravitation in material media , 2011 .

[15]  Chia-Hung Wang,et al.  Optimal multi-level thresholding using a two-stage Otsu optimization approach , 2009, Pattern Recognit. Lett..

[16]  Yanqing Zhang,et al.  Image Thresholding Using Particle Swarm Optimization , 2008, 2008 International Conference on MultiMedia and Information Technology.