Team for Research in
Ubiquitous Secure Technology

Citation
Allen Yang, John Wright, Yi Ma, Shankar Sastry. "Unsupervised Segmentation of Natural Images via Lossy Data Compression". Computer Vision and Image Understanding, January 2007.

Abstract
In this paper, we cast natural-image segmentation as a problem of clustering texture features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. Unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures in an image.We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach. Using either 2D texture filter banks or simple fixed-size windows to obtain texture features, the algorithm effectively segments an image by minimizing the overall coding length of the feature vectors. We conduct comprehensive experiments to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for image segmentation. The algorithm compares favorably against other well-known image-segmentation methods on the Berkeley image database.

Electronic downloads

• http://www.eecs.berkeley.edu/%7Eyang/publications/AllenYangCVIU07.pdf

• HTML

  Allen Yang, John Wright, Yi Ma, Shankar Sastry. <a
  href="http://www.truststc.org/pubs/279.html"
  >Unsupervised Segmentation of Natural Images via Lossy
  Data Compression</a>, <i>Computer Vision and
  Image Understanding</i>, January 2007.

• Plain text

  Allen Yang, John Wright, Yi Ma, Shankar Sastry.
  "Unsupervised Segmentation of Natural Images via Lossy
  Data Compression". <i>Computer Vision and Image
  Understanding</i>, January 2007.

• BibTeX

  @article{YangWrightMaSastry07_UnsupervisedSegmentationOfNaturalImagesViaLossyDataCompression,
      author = {Allen Yang and John Wright and Yi Ma and Shankar
                Sastry},
      title = {Unsupervised Segmentation of Natural Images via
                Lossy Data Compression},
      journal = {Computer Vision and Image Understanding},
      month = {January},
      year = {2007},
      abstract = {In this paper, we cast natural-image segmentation
                as a problem of clustering texture features as
                multivariate mixed data. We model the distribution
                of the texture features using a mixture of
                Gaussian distributions. Unlike most existing
                clustering methods, we allow the mixture
                components to be degenerate or nearly-degenerate.
                We contend that this assumption is particularly
                important for mid-level image segmentation, where
                degeneracy is typically introduced by using a
                common feature representation for different
                textures in an image.We show that such a mixture
                distribution can be effectively segmented by a
                simple agglomerative clustering algorithm derived
                from a lossy data compression approach. Using
                either 2D texture filter banks or simple
                fixed-size windows to obtain texture features, the
                algorithm effectively segments an image by
                minimizing the overall coding length of the
                feature vectors. We conduct comprehensive
                experiments to measure the performance of the
                algorithm in terms of visual evaluation and a
                variety of quantitative indices for image
                segmentation. The algorithm compares favorably
                against other well-known image-segmentation
                methods on the Berkeley image database.},
      URL = {http://www.truststc.org/pubs/279.html}
  }
  

Posted by Allen Yang on 6 Aug 2007.
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