Compressed domain based pornographic image recognition using multi-cost sensitive decision trees

Abstract

In this paper, a novel and effective pornographic image recognition method is proposed. Contributions of this paper include two aspects. (1) Due to the fact that the images are mostly stored and transmitted with JPEG compressed format on Internet, feature extraction is directly performed in the compressed domain. The exacted features include those derived from skin color regions, the results of image retrieval, human face and region of interest, as well as the global features of color and texture. (2) Data mining method is employed to search for the potential decision rules from large-scale image feature sets. Taken the misclassification cost and test cost into account, multi-cost sensitive decision tree is constructed first to improve the recognition speed and accuracy. Furthermore, the concept of pornography degree is introduced into the decision rules, which is output as the recognition results to represent the probability of the image being judged as pornographic. Experimental results show that, the recognition speed of the proposed method is almost three times faster than the classical pixel domain-based recognition method, and the recognition accuracy is also higher in terms of True Alarm Rate (TPR) and False Alarm Rate (FPR).