Study of Foreground Image Segmentation Using Support Vector Machines: Supervised Foreground Image Segmentation Using Support Vector Machines (Svm)

This book gives a detailed investigation of a supervised image segmentation task using Support Vector Machine (SVM). Support vector machine is considered a good candidate because of its good generalization performance, especially when the number of training samples is very small and the dimension of feature space is very high. An SVM based system constructs a hyper-plane in a higher or infinite dimensional space, which can be used for classification, regression, and other tasks. At first, features are extracted from the training images and class labels are assigned accordingly. Using those features, the system is trained. On finishing the training process test samples are fed to the system. As an output, the trained system will classify image pixels into one of those two classes and we will get a segmented image consisting of two distinct classes. Experimental results show that Support vector machine is a promising technique in image segmentation, which is compared with another supervised method, GMM (Gaussian Mixture Model).