Enabling scalable spectral clustering for image segmentation

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摘要

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.

论文关键词:Spectral clustering,Image segmentation,Stochastic ensemble consensus

论文评审过程:Received 1 February 2010, Revised 25 May 2010, Accepted 20 June 2010, Available online 25 June 2010.

论文官网地址:https://doi.org/10.1016/j.patcog.2010.06.015