Image Segmentation of Optimized Combined Kernel Based on Automatic Sample and PSO

Color image segmentation occupies an important position in image processing.To avoid the unreliability of image samples with manual selection,use K-means for image's pre-classification,and then select the image's HSV color space features via Matlab programming automatically.Present the idea of the block: process the color image with partitioning firstly; then output the block images that can be judged as background or foreground directly; use Support Vector Machine( SVM) method for training and segmenting the remaining block images.With the linearly combination of the global and local kernel,select the optimal combination kernel function for image segmentation.Introduce the Particle Swarm Optimization( PSO) to optimize the parameters in combined kernel.The experimental results show that the proposed method is effective.The image segmentation accuracy is satisfactory and stable.