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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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
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