Modified CLPSO-based fuzzy classification System: Color Image Segmentation

Fuzzy segmentation is an effective way of segmenting out objects in images containing varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization(CLPSO) is proposed for pixel classification in HSI color space by selecting a fuzzy classification systemwith minimum number of fuzzy rules and minimum number of incorrectly classified patterns. In the CLPSObased method, each individual of population is considered to automatically generate a fuzzy classification system. Afterwards, an individual member tries to maximize a fitness criterion which is high classificationrate and small number of fuzzy rules. To reduce the multidimensional search space for an M-classclassification problem, the centroid of each class is calculated and then fixed in membership function of fuzzy system. The performance of the proposed method is evaluated in terms of future classification within the RoboCup soccer environment with spatially varying illumination intensities on the scene. The results present 85.8% accuracy in terms of classification.

Keywords:

Comprehensive learning particle swarm optimization , Fuzzy classification , Image segmentation , Robotics , RoboCup , LUT generation , Pattern recognition