Image Mining: A Case for Clustering Shoe Prints

Image Mining: A Case for Clustering Shoe Prints

Sun Wei (Monash University, Australia), Taniar David (Monash University, Australia) and Torabi Torab (La Trobe University, Australia)
DOI: 10.4018/978-1-60566-719-5.ch005
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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.

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