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Automatic View Selection: An Application to Image Mining

  • Conference paper
Advances in Knowledge Discovery and Data Mining (PAKDD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3518))

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Abstract

In this paper we discuss an image mining application of Egeria detection. Egeria is a type of weed found in various lands and water regions over San Joaquin and Sacramento deltas. The challenge is to find a view to accurately detect the weeds in new images. Our solution contributes two new aspects to image mining. (1) Application of view selection to image mining: View selection is appropriate when a specific learning task is to be learned. For example, to look for an object in a set of images, it is useful to select the appropriate views (a view is a set of features and their assigned values). (2) Automatic view selection for accurate detection: Usually classification problems rely on user-defined views. But in this work we use association rule mining to automatically select the best view. Results show that the selected view outperforms other views including the full view.

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References

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Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Manoranjan Dash

  2. Department of Computer Science and Engg., Arizona State University, Tempe, 85387-5406, AZ

    Deepak Kolippakkam

Authors
  1. Manoranjan Dash
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  2. Deepak Kolippakkam
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Editor information

Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Asahidai 1-1, 923-12292, Nomi, Japan

    Tu Bao Ho

  2. University of Hong Kong, Pokfulam Road, Hong Kong, China

    David Cheung

  3. Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona, USA

    Huan Liu

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© 2005 Springer-Verlag Berlin Heidelberg

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Dash, M., Kolippakkam, D. (2005). Automatic View Selection: An Application to Image Mining. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_14

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  • DOI: https://doi.org/10.1007/11430919_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26076-9

  • Online ISBN: 978-3-540-31935-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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