Research on pornographic images recognition method based on visual words in a compressed domain

Abstract

In order to recognise and filter pornographic images, visual-word-based image representation has attracted more and more attention. An image can be represented as a bag of visual words, which is analogous to the bag-of-words representation of text documents. However, most of the existing approaches create visual words from images in the pixel domain, which requires extra processing time to decompress images, since most images are stored in compressed formats. A novel pornographic images recognition method based on visual words in a compressed domain is proposed in this study. There are four steps in this method: (i) low-resolution image is constructed from compressed data; (ii) scale-invariant feature transform (SIFT) descriptors are extracted from this low-resolution image; (iii) a visual vocabulary is created based on SIFT descriptors; (iv) pornographic images are identified by using a support vector machine (SVM) classifier. The experimental results indicate that the proposed method can recognise pornographic images accurately with much less computational time.