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Global and Local Sparse Subspace Optimization for Motion Segmentation : Volume Ii-3/W5, Issue 1 (20/08/2015)

By Ying Yang, M.

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Book Id: WPLBN0004013945
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Global and Local Sparse Subspace Optimization for Motion Segmentation : Volume Ii-3/W5, Issue 1 (20/08/2015)  
Author: Ying Yang, M.
Volume: Vol. II-3/W5, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany

Citation

APA MLA Chicago

Rosenhahn, B., Ackermann, H., Feng, S., & Yang, M. Y. (2015). Global and Local Sparse Subspace Optimization for Motion Segmentation : Volume Ii-3/W5, Issue 1 (20/08/2015). Retrieved from http://worldlibrary.in/


Description
Description: Computer Vision Lab, TU Dresden, Dresden, Germany. In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.

Summary
GLOBAL AND LOCAL SPARSE SUBSPACE OPTIMIZATION FOR MOTION SEGMENTATION

 

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