Enabling scalable spectral clustering for image segmentation

  • Authors:

  • Frederick Tung;Alexander Wong;David A. Clausi

  • Affiliations:

  • Vision and Image Processing Lab, Systems Design Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, Canada N2L 3G1;Vision and Image Processing Lab, Systems Design Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, Canada N2L 3G1;Vision and Image Processing Lab, Systems Design Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, Canada N2L 3G1

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

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.