A comparative study of local feature extraction algorithms for Web pornographic image recognition

Feature extraction algorithm plays an important part in content based pornographic image recognition. In this paper, the performances of six outstanding local feature extraction algorithms are compared and analysed in Web pornographic image recognition. The six algorithms include Scale Invariant Feature Transform, Speeded Up Robust Features, Oriented FAST and Rotated BRIEF, Fast Retina Keypoint, Binary Robust Invariant Scalable Keypoints and KAZE. Through the comparison experiments based on the same image recognition scheme, we conclude that the highest recognition precision can be obtained by SURF, and a good trade-off between recognition speed and precision can be achieved by ORB.