An approach of bag-of-words based on visual attention model for pornographic images recognition in compressed domain

Bag-of-words (BoW) model has been widely used in pornographic images recognition and filtering. Most of existing methods create BoW from images with a scale-invariant feature transform (SIFT) descriptor in the pixel domain. These methods require extra processing time to decompress images in compressed formats. In addition, the SIFT descriptor only views local feature points in centers of some regions as BoW, which ignores a major role of image region in the human visual system. Different from the above methods in this paper, a BoW approach based on the visual attention model is proposed to recognize pornographic images in compressed domain, which includes the following steps: (1) face is detected to remove the face or ID photo from some benign images; (2) a visual attention model is built according to the characteristics of pornographic image; (3) pornographic regions are detected by visual attention model in compressed domain; (4) four features of color, texture, intensity and skin are extracted in pornographic regions; (5) BoW is created by k-means cluster and (6) BoW will be used to represent and recognize pornographic images. Experimental results show that proposed BoW approach based on the visual attention model can more accurately recognize pornographic images with less computational time.

► We extract pornographic image features directly in the compressed domain.
► We use pornographic regions to create bag-of-words.
► We detect pornographic regions based on the visual attention model in compressed domain.
► The recognition speed of the compressed-domain is faster than the pixel-domain.
► The accuracy of pornographic images recognition is higher than SIFT.