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Unsupervised Segmentation of Natural Images via Lossy Data Compression
Allen Yang, John Wright, Yi Ma, Shankar Sastry

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.

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Citation formats  
  • 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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