Other approaches use global dimension minimization to reveal the clusters corresponding to the underlying subspace. These approaches use only two frames for motion segmentation even if multiple frames are available as they cannot use multi frame information. Multiview-based approaches utilize the trajectory of feature points unlike two-view based approaches. A number of approaches have been provided which include Principle Angles Configuration (PAC) and Sparse Subspace Clustering (SSC) methods. These work well in two or three motion cases. These algorithms are also robust to noise with a tradeoff with speed, i.e. they are less sensitive to noise but slow in computation. Other algorithms with a multi-view approach are spectral curvature clustering (SCC), latent low-rank representation-based method (LatLRR) and ICLM-based approaches. These algorithms are faster and more accurate than the two-view based but require greater number of frames to maintain the accuracy. Motion segmentation is a field under research as there are many issues which provide scope of improvement. One of the major problems is of feature detection and finding correspondences. There are strong feature detection algorithms but they still give false positives which can lead to unexpected correspondences. Finding these pixel or feature correspondences is a difficult task. These mismatched feature points from the objects and the background often introduce outliers. The presence of image noise and outliers further affect the accuracy of structure from motion (SFM) estimation. Another issue is that of motion models or motion representations. It requires the motion to be modeled or estimated in the given model used in the algorithm. Most algorithms perform 2-D motion segmentation by assuming the motions in the scene can be modeled by 2-D affine motion models. Theoretically, this is valid because 2-D translational motion model can be represented by general affine motion model. However, such approximations in modeling can have negative consequences. The translational model has two parameters and the affine model has 6 parameters so we estimate four extra parameters. Moreover, there may not be enough data to estimate the affine motion model so the parameter estimation might be erroneous. Some of the other problems faced are:   Robust algorithms have been proposed to take care of the outliers and implement with greater accuracy. The Tomasi and Kanade factorization method is one of the methods as mentioned above under factorization. Motion segmentation has many important applications. It is used for video compression. With segmentation, it is possible to eliminate the redundancy related to the repetition of the same visual patterns in successive images.