Image segmentation with color and texture using RBFNN minimizing the L-GEM

The Internet provides a huge source of images. Not all of them are professionally edited or well organized. This raises the need of image classification and indexing to enhance the efficiency of using those images. To improve the image classification accuracy, image segmentation is important to remove background and noisy parts in an image. In this paper, we propose an image segmentation method by radial basis function neural network (RBFNN) based on the localized generalization error model (L-GEM). Pixels are classified as target object and background by the RBFNN. Color, gradients and texture are used as features for a pixel. Car images are adopted and we target to separate the car from its background and overlapping objects. Comparison of different neighboring size is conducted. In this pilot study, 11times11 is found to be appropriate size for car segmentation.