PLSA Model Based Detection Of Porn Pictures

With the rapid development of information and Internet technology in the 21st century, the Networked information has become as the convenient information source, and the big part of it is in the form of graphics. As the increasing of networked information, the amount of images presented in network will also increase. And the large amounts of them have included porn images, which terribly influences the networked life of peoples particularly the teenagers. In order to resolve the problem of having porn images on the Internet, researchers have proposed the filtering technology based on the image content. In the process of content-based image filtering, the feature extraction of images and a good filtering strategy became the key to success. According to these two points, a combination of pictures filtering model which bases on feature extraction algorithm, says SIFT, and a feature matching algorithm, says plsa are presented in this paper.This paper discuses the plsa matching algorithm as the primary algorithm, which is due to sensitive image filtering is a problem of small samples. The model of Probabilistic Latent Semantic Analysis (plsa) has great advantage for solving those problems with small samples, nonlinear and higher dimensional pattern recognition, and classification. In addition, this model has the ability of learning and memory.The proposed image filtering model aims to improve the performance of the filter. The technique presented here is to combine with the plsa model and SIFT method on images. This is based on the consideration of that SIFT is equivalent to a high-pass filter when the difference Gaussian operation is applied on images, which can filter low-frequency components from the image background. In other words, the SIFT features are not presented in the pure color and smooth background, so that the characteristics of the image can be highlighted and reflect the main contents of the image. Therefore, the mismatch between the features of sample images and actual images is reduced. The traditional plsa algorithm and the one proposed in this paper have been tested under the same condition, and the experimental results show that the proposed algorithm is better than the traditional one.This paper has shown the following contributions to detection of porn images:1. According to characteristics of human sensitive area, the visual words of images from a training library are optimized, which can reduce the number of visual words in the training library, and improve the system performance in terms of the efficiency of matching.2. This model is a combination of the plsa and SIFT algorithm applied to image filtering. The SIFT algorithm can satisfy to Scale-invariant feature transform and this algorithm has strong ability to solve the problem of cover objects, so that which can reduce the influence of background on feature matching. Therefore, the influence of image background on filtering, when only the PLSA algorithm is used to the image filtering, is removed.