A modified support vector machine and its application to image segmentation

Recently, researchers are focusing more on the study of support vector machine (SVM) due to its useful applications in a number of areas, such as pattern recognition, multimedia, image processing and bioinformatics. One of the main research issues is how to improve the efficiency of the original SVM model, while preventing any deterioration of the classification performance of the model. In this paper, we propose a modified SVM based on the properties of support vectors and a pruning strategy to preserve support vectors, while eliminating redundant training vectors at the same time. The experiments on real images show that (1) our proposed approach can reduce the number of input training vectors, while preserving the support vectors, which leads to a significant reduction in the computational cost while attaining similar levels of accuracy. (2)The approach also works well when applied to image segmentation.

Research Highlights
► A modified support vector machine is designed, which is based on the properties of support vectors and a pruning strategy.
► The modified SVM will preserve support vectors, while eliminating redundant training vectors at the same time.
► The modified SVM works well when applied to image segmentation.