Adult Image Detecting Algorithm Based On Sparse Representation Of Image Semantic Information

People can get plenty of useful information through high-quality multimedia communications. However, the transmission of pornographic and other undesirable information is also more subtle and concealed. This trend leads to serious social problems. Therefore, the research achievements for this purpose are helpful to purify the social environment, protect the mental health of the underages, and safeguard social ethics. The existing methods are systemically reviewed in this paper. Some crucial problems are discussed, such as, robust skin modeling, feature representation and evaluation of pornographic images. The main achievements of this paper are summarized as follows.Since pornographic images always contain skin area, we proposed an active learning based Bayes discriminative model for skin detection. According to the similar mechanism of perception between the human visual system and YCbCr color space, and the existence of clustering trend in YCbCr color space, we choose the YCbCr color space to build our Bayes discriminative skin model. We further incorporate the Bootstrap method to improve the diversity and typicality of training samples to avoid model overfitting.To improve the accuracy of image determination, we introduce the high level semantic information to the skin area analysis. The difference of Gaussian based extreme points detection operator is used to extract the is invariant to sifting, rotation, scale, brightness, occlusion and noise changes, and can maintain the stability for translation affine in certain degree. We adopt SIFT descriptor to represent the interesting points of the images, which can reflect the characteristics of the adult images better.To handle the diversity of the test image, we propose a bag of words model and sparse representation based method for adult image evaluation. SIFT features are sensitive to points matching. We regard the images as the combination of the visual words, which come from the codebook. Under different distance measurements, the SIFT features are clustered by K-means clustering algorithm to reduce the feature sensitivity, through which the codebooks of normal images and adult images are formed, respectively. Then we represent the test images on these two codebooks by two reconstruction errors.This work abstracts scientific issues from the practical applications. It involves the latest theories in computer vision and statistical machine learning areas. This research is forward-looking and challenging, it has an extremely important theoretical and practical value.