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Image Mining: A Case for Clustering Shoe prints

Author

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  • Wei Sun

    (Monash University, Australia)

  • David Taniar

    (Monash University, Australia)

  • Torab Torabi

    (La Trobe University, Australia)

Abstract

Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, once analysed, can reveal useful information to our uses. The focus for image mining in this article is clustering of shoe prints. This study leads to the work in forensic data mining. In this article, we cluster selected shoe prints using k-means and expectation maximisation (EM). We analyse and compare the results of these two algorithms.

Suggested Citation

  • Wei Sun & David Taniar & Torab Torabi, 2008. "Image Mining: A Case for Clustering Shoe prints," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 3(1), pages 70-84, January.
    • Handle: RePEc:igg:jitwe0:v:3:y:2008:i:1:p:70-84
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