Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/67311
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Type: Conference paper
Title: Boosted Cannabis Image Recognition
Author: Xie, N.
Li, X.
Zhang, X.
Hu, W.
Wang, J.
Citation: Proceedings of the 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008 / pp.1-4
Publisher: IEEE
Publisher Place: Online
Issue Date: 2009
ISBN: 9781424421756
ISSN: 1051-4651
Conference Name: International Conference on Pattern Recognition (19th : 2008 : Tampa, Florida)
Statement of
Responsibility: 
Nianhua Xie, Xi Li, Xiaoqin Zhang, Weiming Hu, James Z. Wang
Abstract: With the large number of Web sites promoting the use of illicit drugs, it has become important to screen these sites for the protection of children on the Internet. Conventional keyword-based approaches are not sufficient because these Web sites often have lots of images and little meaningful words than prices. We propose an AdaBoost-based algorithm for cannabis image recognition. This is the first known attempt at computerized detection of illicit drug Web contents using images. The main technical contributions of our work are two-fold. First, we introduce a novel weak classifier which considers the inherently structural property or ldquoself-similarityrdquo of the cannabis plants. The self-correlation structural characteristics of cannabis can be used as a discriminative property for the purpose of cannabis image recognition. Second, we propose a rapid weak classifier finder, which can efficiently select discriminative weak classifiers from the weak classifier space with little degradation to the classification accuracy. Experiments on real world images have demonstrated improved performance of our method over other methods.
Rights: ©2008 IEEE
RMID: 0020112688
DOI: 10.1109/ICPR.2008.4761592
Appears in Collections:Computer Science publications

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