Harmful Content Detection Based on Cascaded Adaptive Boosting

Abstract

Recently, it has become very easy to acquire various types of image contents through mobile devices with high-performance visual sensors. However, harmful image contents such as nude pictures and videos are also distributed and spread easily. Therefore, various methods for effectively detecting and filtering such image contents are being introduced continuously. In this paper, we propose a new approach to robustly detect the human navel area, which is an element representing the harmfulness of the image, using Haar-like features and a cascaded AdaBoost algorithm. In the proposed method, the nipple area of a human is detected first using the color information from the input image and the candidate navel regions are detected using positional information relative to the detected nipple area. Nonnavel areas are then removed from the candidate navel regions and only the actual navel areas are robustly detected through filtering using the Haar-like feature and the cascaded AdaBoost algorithm. The experimental results show that the proposed method extracts nipple and navel areas more precisely than the conventional method. The proposed navel area detection algorithm is expected to be used effectively in various applications related to the detection of harmful contents.