Particle Swarm Optimization for Pattern Recognition and Image Processing

Summary. Pattern recognition has as its objective to classify objects into different categories and classes. It is a fundamental component of artificial intelligence and computer vision. This chapter investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing. First a clustering method that is based on PSO is discussed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. Then PSObased approaches that tackle the color image quantization and spectral unmixing problems are discussed.

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