NDLI: Self-Organizing Map Based Multiscale Spectral Clustering for Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Ying Duan Tao Guan Lei Liu
Copyright Year	2012
Abstract	Spectral clustering receives wide attention in recent years since its efficiency in image segmentation and irregular data clustering. However, the applications of it in large scale data processing, such as web data categorization and image segmentation, are greatly restricted because of its O(n3) computational complexity. To address this problem, we propose a new effective scheme to greatly decrease the complexity while keep the clustering quality. The scheme adopts the Self-Organization Map(SOM) to encode the original data and then groups the obtained prototypes using multiscale spectral clustering proposed by us. We analyze and compare the performance of our approach with NJW and find that ours has less time consumption. Furthermore, we carry out an experiment on color image segmentation and results show that our approach behaves better than Kmeans algorithm.
Starting Page	329
Ending Page	333
File Size	454450
Page Count	5
File Format	PDF
ISBN	9781467306898
e-ISBN	9780769546476
DOI	10.1109/ICCSEE.2012.375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Image segmentation Prototypes Clustering algorithms Color Clustering analysis Vectors Spectral clustering Complexity theory
Content Type	Text
Resource Type	Article

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