Image Mining: A Case for Clustering Shoe prints

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Information Technology and Web Engineering Pub Date : 2008-01-01 DOI:10.4018/jitwe.2008010105

Wei Sun, D. Taniar, T. Torabi

引用次数: 32

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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图像挖掘:聚类鞋印的案例

图像采集和存储技术的进步导致了非常庞大和详细的图像数据库的巨大增长。这些图像一经分析，就可以为我们提供有用的信息。本文中图像挖掘的重点是鞋印的聚类。本研究为法医数据挖掘的研究提供了基础。在本文中，我们使用k-means和期望最大化(EM)对选定的鞋印进行聚类。我们对这两种算法的结果进行了分析和比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Information Technology and Web Engineering COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

2.60

自引率

0.00%

发文量

期刊介绍： Organizations are continuously overwhelmed by a variety of new information technologies, many are Web based. These new technologies are capitalizing on the widespread use of network and communication technologies for seamless integration of various issues in information and knowledge sharing within and among organizations. This emphasis on integrated approaches is unique to this journal and dictates cross platform and multidisciplinary strategy to research and practice.