Journal
PATTERN RECOGNITION
Volume 43, Issue 12, Pages 4069-4076Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.06.015
Keywords
Spectral clustering; Image segmentation; Stochastic ensemble consensus
Funding
- Natural Sciences and Engineering Research Council (NSERC) of Canada
- GEOIDE (Geomatics for Informed Decisions, a Network of Centres of Excellence)
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Spectral clustering has become an increasingly adopted tool and an active area of research in the machine learning community over the last decade. A common challenge with image segmentation methods based on spectral clustering is scalability, since the computation can become intractable for large images. Down-sizing the image, however, will cause a loss of finer details and can lead to less accurate segmentation results. A combination of blockwise processing and stochastic ensemble consensus are used to address this challenge. Experimental results indicate that this approach can preserve details with higher accuracy than comparable spectral clustering image segmentation methods and without significant computational demands. (C) 2010 Elsevier Ltd. All rights reserved.
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