TY - GEN
T1 - Boosted cannabis image recognition
AU - Xie, Nianhua
AU - Li, Xi
AU - Zhang, Xiaoqin
AU - Hu, Weiming
AU - Wang, James Z.
PY - 2008
Y1 - 2008
N2 - With the large number of Websites 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 Websites 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 "self-similarity" 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.
AB - With the large number of Websites 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 Websites 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 "self-similarity" 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.
UR - http://www.scopus.com/inward/record.url?scp=77957930874&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:77957930874
SN - 9781424421756
T3 - Proceedings - International Conference on Pattern Recognition
BT - 2008 19th International Conference on Pattern Recognition, ICPR 2008
T2 - 2008 19th International Conference on Pattern Recognition, ICPR 2008
Y2 - 8 December 2008 through 11 December 2008
ER -