Content Based Net Filtering Methods Study

Along with the mature of network technology, all kinds of the resources have been increased multiples. Especially in picture and video message area. As a result porn message have overflowed in network and become a global hazard. Public have a strong interest in image filter based on its content to forbid this un-healthy message spread in network and rationalize the relations of network. And a lot of scholars which include business market have a deep research in image filter field, of course some commerce image filter software have been sold by company. But there have a bottleneck in filter which based on website and sensitive key works to block the un-healthy message. It brings some shortages like hysteretic, high fallout ration and high miss detection rates, etc. But most of un-healthy information is spread by images. We need raise a research direction on image filter based on content which can solve the root cause of the shortage of network safety technology on image filter and monitor. This paper concentrate on the image filter which based on the former scholars'work, combine with mine insight in image content and filter theory, then raise a new and effective filter tool for image. The research works are list below:(1) Image format analysis. This paper have introduced popular image format at the moment: BMP,TIFF,GIF and JPEG, etc. And concentrate on the loss compression image which has a kind of current popular standard JPEG image. Given a brief introduction in compression and decompression theory which as a theory basement for image filter.(2) Research on images'color space and skin detection model's choice. Images'color space include RGB,YCbCr,YIQ,YUV,YCgCr, etc. There are also have an analysis on transform methods on different color space and skin pixel distributes compression level in different color space. There are also having introductions on three skin detection models: threshold skin detection model, statistics histogram skin model and mixed gauss skin model. And through a skin pixel test to get the examining rate to compare the three models'merit rating which offer the groundwork on illumination adaptive skin color detection method.(3) Skin texture and image classification model analysis. Skin texture method include first order gray matrix, calculate the skin samples gray, then get the skin texture mean statistics to compare with the threshold value to get the result. Gray level co occurrence matrix in math angle to research images texture gray grade unite distribution for reaction the textures space inter rely on relation ship. Gabor filter method use different frequent region messages to reflect the image texture part of features. Image classifications include support vector machine, decision tree and BP nerve network classification.(4) According to previous background analysis on image content detection, skin pixel verification and image classification. There have a deep insight on image filter based on its'content. And got a conclusion on images'classification. This paper raise two detect methods on image filter: illumination adaptive skin color detection method which concentrate on improve are examine rate, fuzzy cognitive map based skin detection method in compressed domain which focused on detection rate. Follow by an experiment to verify the filter rate, and test result prove two different methods all get a well result on detection. Through the experiment method demonstrate the nude image filter method based on image content which raised in this paper get very well detection result.