Boosted cannabis image recognition

Nianhua Xie, Xi Li, Xiaoqin Zhang, Weiming Hu, James Z. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
StatePublished - 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: Dec 8 2008Dec 11 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other2008 19th International Conference on Pattern Recognition, ICPR 2008
Country/TerritoryUnited States
CityTampa, FL

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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