Image mining: issues, frameworks and techniques

Zhang, Ji and Hsu, Wynne and Lee, Mong Li (2001) Image mining: issues, frameworks and techniques. In: 2nd ACM SIGKDD International Workshop on Multimedia Data Mining (MDM/KDD'01), 26 Aug 2001, San Francisco, CA, USA.

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[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an
interdisciplinary endeavor that draws upon expertise in
computer vision, image processing, image retrieval, data
mining, machine learning, database, and artificial
intelligence. Despite the development of many
applications and algorithms in the individual research
fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information (displayed to public): No evidence of copyright restrictions.
Depositing User: Dr Ji Zhang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 28 Sep 2009 05:44
Last Modified: 02 Jul 2013 23:23
Uncontrolled Keywords: image mining, image indexing and retrieval, object recognition, image classification, image clustering, association rule mining
Fields of Research (FoR): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining

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