The Design And Implementation Of A Filter Of Erotic Images Based On IE

Internet has become the best way to get knowledge and broaden views. It is a medium spread means and communication tool. But it also brings the problem of erotic novels and images spread. Some anti-porn filter software is designed. But the algorithms of the software are based on IP address filter or web page text content judgement. The former shields messages by IP address matching, it needs to change the IP address database by the collected termly IP addresses, so it lags evidently. The later may neglect some sites and shield some good sites because of the limitation of text content.Only content-based analysis and comprehension can radically solve the problem that net-security technology has short ability of image information filter and supervision. This filter uses content-based image filtering technology. It adopts complexion detection and texture procession.Erotic image filter is a problem of image comprehension and recognition, but it is different from face recognition and fingerprint recognition. It is difficult to show all the properties by a simple model because of the complex background of images and different illumination condition and diversities of body exhibiting form. Erotic images are characteristic of bareness skin, so we use skin detecting models, texture models and corresponding classing algorithms to make the filter model.Color information is usually an effective feature in image division. The lightness of the object color is very different under different illumination condition, but the chroma is constant and keeps fixed generally. Complexion has yellow, purple, black, white and so on. It corresponds with some distributing area in color spaces and forms certain patterns.YUV and YIQ are two color spaces often used in image processing. Chroma space model adopts traits in skin color of YUV and YIQ color spaces. In YUV color space, Y denote brightness, U and V are chroma signals. It is a two-dimension vector called chroma signal vector. Every color corresponds a chroma signal vector. Model of Ch is its saturation, the angle of θ is hue. Image division by the character of θ can filter the background which is very different from skin color, but it cannot divide skin from the purple hair and gray background. We can increase the division effect by the saturation information in YIQ space because human face has much yellow weight. I weight is the hue from orange yellow to green. The less it is, the more yellow it has, and the less green it has. We can confirm the range of color information distributing by the angle θin YUV space and I in YIQ space. Stat shows that the range of complexion hue is regular. The range of θ is [100,150], the range of I is [20,90].We can get primary mask images by processing the images through complexion model. But they may have error and maybe much because of the similitude between non-skin objects and skin. So we need adopt texture model to process the mask images based on the previous step. Non-skin skin area will be eliminated, the skin area in erotic images will be exact, the just detecting ratio is increased, and the error ratio is decreased.Vision faces all have texture traits, which include the information about the object structure and the relation between objects and circumstance. Texture model needs to analyse the texture traits of skin pixels for intercommunity. It can eliminate the error area in skin detecting and keep the right area.The gray stat method is basis in image processing. In the implementation, we use the idea of one-rank-gray stat to judge whether a point and around part are smooth in skin area. First, we get the area stat gray value, generally it is average value or the gray value of the pixel in the area center, second compare the pixel value in the area with the stat value to get the traits, and then judge whether it is consistent with the stat traits of the stat skin area.We should get eigenvalues from the images based on the mask images to provide a good classing vector for the filter. In the image indexing and image s...