Recognizing pornographic images

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.