Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SHALLOW STRUCTURE METHODS <s> We present an unsupervised approach for learning a generative layered representation of a scene from a video for motion segmentation. The learnt model is a composition of layers, which consist of one or more segments. Included in the model are the effects of image projection, lighting, and motion blur. The two main contributions of our method are: (i) a novel algorithm for obtaining the initial estimate of the model using efficient loopy belief propagation; and (ii) using /spl alpha//spl beta/-swap and /spl alpha/-expansion algorithms, which guarantee a strong local minima, for refining the initial estimate. Results are presented on several classes of objects with different types of camera motion. We compare our method with the state of the art and demonstrate significant improvements. <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SHALLOW STRUCTURE METHODS <s> 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. <s> BIB002
For shallow structure based methods, Kumar et al. BIB001 developed an unsupervised learning method, which learns a generative layered representation of a scene from a moving camera video for moving object segmentation. This method can be applied to videos containing piecewise parametric motion without restrictions on the type of camera motion. It is also effective to deal with illumination changes, occlusion, lighting and motion blur. Parikh and Maradia BIB002 introduced two kinds of traditional supervised learning algorithms (SVM and LS-SVM) for image segmentation with motions. Segmentation can be performed through selecting image features. They treat the intensity change basis of motion as texture feature for segmentation. Experimental results demonstrate that machine learning approaches are very promising techniques for doing segmentation with motions in the image. That is for segmenting moving object segmentation in the scene.