Boosted cannabis image recognition

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 selfcorrelation 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 degra...

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: J. Z. Wang, Nianhua Xie, Weiming Hu, Xi Li, Xiaoqin Zhang
• Keywords: Computer Vision, Discriminative Weak Classifiers, ICPR 2008, Weak Classifier Finder, Weak Classifier Space