Survey Of Study On Image Segmentation Based On SVM

Image segmentation and object classification are two important topics of digital image processing. Traditional classification approaches based on statistical theory have been extensively applied in the two research areas. Traditional classification approaches, which are based on the principle of Experiential Risk Minimization instead of Expected Risk Minimization, achieve the best, when the number of training samples is infinite. Because the number of training samples is often limited and data dimension is high, the performance of traditional classification approaches is often unsatisfied in practice.Compared with statistical theory, statistical learning theory focuses on the machine learning of small sample size and can trade off between the complexity of models and generalization performance. Support vector machines, which are based on Vapnik-Chervonenkis (VC) dimension theory and Structural Risk Minimization principle, are considered good candidates because of their high generalization performance without the need to add a priori knowledge, even when the dimension of the input space is very high and the problem is nonlinear.Support vector machine(SVM) is a new sort of recognizing technology. Based on the principle of structural risk minimization instead of the principle of experiential risk minimization, combining the techniques of statistical learning, machines learning and neural networks etc, support vector machines has good capability of generalization. Because of having self-contained theories and good experimental results, Support vector machines are coming researched by more and more researcher.This dissertation surveys of study on image segmentation based on support vector machines. The main contents are as follows:The background of the dissertation is introduced in chapter 1. The definitions and techniques of image segmentation are also summarized. The main statistical learning theory is given in chapter 2. Chapter 3 surveys the support vector machine such as its basic ideas, algorithms, and characters. The methods of image segmentation based on support vector machine are expounded in chapter 4. At last, tags are made in chapter 5.