Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/67311
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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 |
DOI: | 10.1109/ICPR.2008.4761592 |
Published version: | http://dx.doi.org/10.1109/icpr.2008.4761592 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.