Combining multiple SVM classifiers for adult image recognition

Pornographic image recognition and filtering are of great significance for web security and content monitoring. In this paper, an adult image recognition method based on support vector machine (SVM) and erotic category is proposed. Global color and texture features and local SIFT feature are extracted to train multiple SVM classifiers for different erotic classes. Face detection is used to filter out normal close-up images. Four later fusion schemes are presented to determine the final result. A large scale test on 50,000 web images shows the proposed algorithm achieves 12.32% false positive rate(/p) and 14.17% false negatives rate(/h), which is better than five existing methods.