NDLI: Parallel Sparse Spectral Clustering for SAR Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Shuiping Gou Xiong Zhuang Huming Zhu Tiantian Yu
Copyright Year	2008
Abstract	A novel parallel spectral clustering approach is proposed by exploiting the distributed computing in MATLAB for SAR image segmentation quickly and accurately. For large-scale data applications, most existing spectral clustering algorithms suffer from the bottleneck problems of high computational complexity and large memory use. And in the absence of advanced hardware and software equipments with only the loosely coupled computer resources accessible, the framework of MATLAB Parallel Computing-based sparse spectral clustering is constructed in this paper. In the proposed frame, we use a distributed parallel computing model to accelerate computation, where each partition of data instances is assigned to different processor nodes for the similarity matrix calculation in spectral clustering. Further, by the construction of exact t-nearest neighbor sparse symmetric similarity matrix, the sparseness technique is employed to alleviate the storage stress. Besides, the problems of how to choose the number of nearest neighbors and the scaling parameter are also discussed. The segmentation results on artificial synthesis texture images and SAR images show that the proposed parallel algorithm can effectively handle large-size segmentation cases. Meanwhile, it can obtain better segmentation results compared with Nyström approximation spectral clustering and k-means clustering algorithm.
Starting Page	1949
Ending Page	1963
Page Count	15
File Size	3969176
File Format	PDF
ISSN	19391404
Volume Number	6
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Image segmentation Sparse matrices MATLAB Parallel processing Synthetic aperture radar Symmetric matrices spectral clustering Exact t-nearest neighbors image segmentation parallel computing sparse representation
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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