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A compression-based distance measure for texture

Authors

  • Bilson J. L. Campana,

    Corresponding author
    1. Department of Computer Science and Engineering, University of California, Riverside, 900 University Ave., Riverside, CA, USA
    • Department of Computer Science and Engineering, University of California, Riverside, 900 University Ave., Riverside, CA, USA
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  • Eamonn J. Keogh

    1. Department of Computer Science and Engineering, University of California, Riverside, 900 University Ave., Riverside, CA, USA
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Abstract

The analysis of texture is an important subroutine in application areas as diverse as biology, medicine, robotics, and forensic science. While the last three decades have seen extensive research in algorithms to measure texture similarity, almost all existing methods require the careful setting of many parameters. There are many problems associated with a lot of parameters, the most obvious of which is that with many parameters to fit, it is very difficult to avoid overfitting. In this work, we propose to extend recent advances in Kolmogorov complexity-based similarity measures to texture matching problems. These Kolmogorov-based methods have been shown to be very useful in intrinsically discrete domains such as DNA, protein sequences, MIDI music, and natural languages; however, they are not well defined for real-valued data. To address this, we introduce a very simple idea, the Campana-Keogh (CK) video compression-based method for texture measures. These measures utilize video compressors to approximate the Kolmogorov complexity. Using the parameter-free CK method, we novely utilize lossy compression to create an efficient and robust parameter-lite texture similarity measure: the CK-1 distance measure. We demonstrate the utility of our measure with extensive empirical evaluations on real-world case studies drawn from nematology, arachnology, entomology, medicine, forensics, texture analysis benchmarks, and many other domains. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 381-398, 2010

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