การหาคุณลักษณะพิเศษเฉพาะพื้นที่สำหรับการจัดหมวดหมู่รูปภาพลามกอนาจาร.

In this paper, we propose a local descriptors approach to classify pornographic images. Two local descriptors including the scale-invariant feature transform (SIFT) and the histogram of oriented gradients (HOG) are computed feature vectors from pornographic images. The extracted features are supplied to the K-Nearest Neighbor Algorithm and Support Vector Machine for pornographic image classification. We have evaluated all different methods on the TI-UNRAM dataset. The results show that the HOG and the SIFT combined with the SVM significantly outperform the other methods, including KNN algorithm and image processing technique. Thus, the local descriptors approach can be combined with the SVM for pornographic image classification in order to obtain the effective classification performance. [ABSTRACT FROM AUTHOR]