Boosted Cannabis Image Recognition

Nianhua Xie, Xi Li, Xiaoqin Zhang, Weiming Hu
Institute of Automation, Chinese Academy of Sciences

James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Abstract:

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.


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Citation: Nianhua Xie, Xi Li, Xiaoqin Zhang, Weiming Hu and James Z. Wang, ``Boosted Cannabis Image Recognition,'' Proceedings of the International Conference on Pattern Recognition, pp. -, Tampa, Florida, December 2008.

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