Based On Multi-instance Learning Image Content Filtering Algorithm Research

This paper unifies the Multi-Instance Learning(MIL) algorithm, studys and has realized a pornographic image supervisory system which is based on the content of image. And it has carried on an exploration in theory and practice to the MIL in the image filtration domain's application.Feature extraction is an important means of the machine to obtain of image content. In view of the pornographic image's characteristic, this article selectes color, texture and shape features as feature values. Feature vectors are made up from these feature values and learned by MIL. Filtration of image is realized by the prediction of the MIL to the unknown concept bags.In this paper, the machine learning process is completed by MIL. Firstly, the concept of image filtration is unified into the framework of MIL. The solution of pornographic image feature vectors istransformed into the problem of target concept searching, which was realized under the framework by the EM_DD algorithm. With the Improvement of the simulation annealing algorithm, the search speed and the precision was enhanced.Through the detection on 1500 pornographic images and 1500 normal images, it is known that the detection rate is 87.4%, the false alarm rate is 12.5%,. According to detection result, the pornographic image filtration algorithm in this paper can effectively identify pornographic images and normal image.Finally, this article developed a pornographic image Monitoring system, which was developed in the Visual C + + 6.0 environment with OOP. It not only has various detection functions, but also can monitor the web browser in real-time. Besides, the system can also record, report, rate etc. the erotic websites.