Abstract. This paper presents a new approach on nipple detection for adult content recognition, it combines the advantage of Adaboost algorithm that is rapid speed in object detection and the robustness of nipple features for adaptive nipple detection. This method first locates the potential nipple-like region by using Adaboost algorithm for fast processing speed. It is followed by a nipple detection using the information of shape and skin color relation between nipple and non-nipple region. As this method uses the nipple features to conduct the adult image detection, it can achieve more precise detection and avoids other methods that only detect the percentage of exposure skin area to decide whether it is an adult image. The proposed method can be also used for other organ level detection. The experiments show that our method performs well for nipple detection in adult images.
1 Introduction
There are a huge number of adult images that can be freely accessed in multimedia documents and databases through Internet. To protect children, detection and blocking the obscene images and videos received more and more concern. Automatic recognition of pornographic images has been studied by some researchers. Current methods can be briefly classified into two kinds [1]: (1) Skin-based detection and (2) Feature-based detection.
Skin-based methods focus on skin detection. Many skin models have been developed based on color histogram [1], chromatic distribution [2], color and texture information [3][5][6][8][9]. After skin region has been detected, perform one of below detections: (a) Model-based detection [3] which is using a geometrical model to describe the structure or shape of human body; (b) Region-based detection which extracts features for recognition based on the detected skin regions. These features include contour and contour-based features [1][8], shape features [2][6], a series of features [9] from each connected skin region: color, texture, and shape, etc. Featurebased methods focus on using the features directly extracted in the images. These features include normalized central moments and color histogram [4], shape feature (Compactness descriptor) [7], etc. These methods tend to use a global matching rather than a local matching. All existing methods mentioned above suffer from a fundamental problem that they did not conduct the detection at the organ (object) level. A certain percentage of skin detected over the whole image or a human body does not mean it is a naked adult image. To make a correct judgment, the basic rule is checking whether the female nipples, male and female private parts are exposure into the image. The only paper can be found in literature that detects the sex organ is in [10] for nipple detection. This method conducted the skin detection first, and then performed the nipple detection using self-organizing map neural network. They claimed thatthe correct nipple detection rate is 65.4%.
There are a huge number of adult images that can be freely accessed in multimedia documents and databases through Internet. To protect children, detection and blocking the obscene images and videos received more and more concern. Automatic recognition of pornographic images has been studied by some researchers. Current methods can be briefly classified into two kinds [1]: (1) Skin-based detection and (2) Feature-based detection.
Skin-based methods focus on skin detection. Many skin models have been developed based on color histogram [1], chromatic distribution [2], color and texture information [3][5][6][8][9]. After skin region has been detected, perform one of below detections: (a) Model-based detection [3] which is using a geometrical model to describe the structure or shape of human body; (b) Region-based detection which extracts features for recognition based on the detected skin regions. These features include contour and contour-based features [1][8], shape features [2][6], a series of features [9] from each connected skin region: color, texture, and shape, etc. Featurebased methods focus on using the features directly extracted in the images. These features include normalized central moments and color histogram [4], shape feature (Compactness descriptor) [7], etc. These methods tend to use a global matching rather than a local matching. All existing methods mentioned above suffer from a fundamental problem that they did not conduct the detection at the organ (object) level. A certain percentage of skin detected over the whole image or a human body does not mean it is a naked adult image. To make a correct judgment, the basic rule is checking whether the female nipples, male and female private parts are exposure into the image. The only paper can be found in literature that detects the sex organ is in [10] for nipple detection. This method conducted the skin detection first, and then performed the nipple detection using self-organizing map neural network. They claimed thatthe correct nipple detection rate is 65.4%.
This paper focuses on nipple detection in images. It is a fundament step in pornography image detection. Our method is an organ model driven, that means we emphasize the features of organ to be detected. In nipple detection, shape and skin are the most important features for nipple appearance. Therefore, in the real application, both of them should be combined for detection, at least play the same important role. Our method consists of two stages:
(1) Rapid locating for potential nipple region. Adaboost algorithm with Haar-like
features is used to rapidly locate the possible nipple regions.
(2) Nipple detection which combines shape and skin statistical information is applied to determine whether the located regions from stage 1 are the real nipples.
The remaining structure of this paper is arranged as follows. Section 2 briefly introduces the Adaboost algorithm with Haar-like features and its application in searching the possible nipple region. Section 3 describes the details of the nipple model for nipple detection. Experimental results and discussion are presented in Section 4. Finally, the conclusion of this paper is presented in Section 5.
by Yue Wang, Jun Li, HeeLin Wang, and ZuJun Hou, Institute for Infocomm Research | Read more (pdf):
Image via: Institute for Infocomm Research
Image via: Institute for Infocomm Research
