Comparative Analysis of Motion base Image Segmentation using Machine Learning Techniques

Objectives: In the computer vision task, motion based image segmentation is the foremost step which warrants for further research. Several progressive techniques are being created for motion base image segmentation. Machine learning methods are implemented and analyzed in this paper. Methods: Motion is one of the useful characteristics to segment object from the image as it provides a classification of pixel in motion or motionless type. Machine learning methods are giving promising result in classification type problems. Machine learning methods reduce the execution time. In this type of technique a training is required in beginning after that all processes are performed automatically. This paper presents two machine learning technique SVM and LS-SVM. The Segmentation of image can be done by selecting image feature. In proposed method intensity change basis of motion is used as texture feature. Findings: Both of techniques classify pixels of an image between two types, 1. Appropriate to a motion and 2. Not appropriate to motion. Results obtained using this approach shows that machine learning methods are very promising techniques for this type of area. Application/Improvement: LS-SVM method is very promising with enhancement in execution time and segmented object result quality as compared to SVM.