C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
System for Screening Objectionable Images Using Daubechies' Wavelets and Color Histograms
IDMS '97 Proceedings of the 4th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
Classifying Objectionable Websites Based on Image Content
IDMS '98 Proceedings of the 5th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
Geometric feature-based skin image classification
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
InFeRno: an intelligent framework for recognizing pornographic web pages
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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We present a novel algorithm for discriminating pornographic and assorted benign images, each categorized into semantic subclasses. The algorithm exploits connectedness and coherence properties in skin image regions in order to capture alarming Regions of Interest (ROIs). The technique to identify ROIs in an image employs a region-splitting scheme, in which the image plane is recursively partitioned into quadrants. Splitting is achieved by considering both the accumulation of skin pixels and texture coherence. This processing step is proven to significantly boost the accuracy and reduction of running time demands, even in the presence of sparse noise due to errors attributed to skin segmentation. For detected ROIs, we extract 15 rough color and spatial features computed from the pixels residing in the ROI. A novel classification scheme based on a tree-structured ensemble of strong Random Forest classifiers is also proposed. The method achieves competitive performance both in terms of response time and accuracy when compared to the state-of-the-art.