It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a classifier based on one of the recently flourishing deep learning techniques. Convolutional neural networks contain many layers for both automatic features extraction and classification. The benefit is an easier system to build (no need for hand-crafting features and classifiers). Additionally, our experiments show that it is even more accurate than the state of the art methods on the most recent benchmark dataset.
Historically, the most investigated feature was the human skin color @cite_0 . If the input image contains too much skin colored pixels, it was taken as an indicator of nudity. However, skin color solely is not reliable since a face closeup image has a lot of skin pixels while being non-porn. So, researchers augmented the skin color with other constraints or shape features @cite_3 . With the introduction of new computer vision models, porn detection algorithms became more accurate. For instance, introduced classification accuracy improvement by adopting the visual bag-of-words (BoW) model @cite_11 . BoW tries to extract the most common patches that exist on a set of training images. For a detailed survey of existing methods, please refer to @cite_6 .
@cite_0 @cite_6 @cite_3 @cite_11
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Abstract With the growing amount of inappropriate content on the Internet, such as pornography, arises the need to detect and filter such material. The reason for this is given by the fact that such content is often prohibited in certain environments (e.g., schools and workplaces) or for certain publics (e.g., children). In recent years, many works have been mainly focused on detecting pornographic images and videos based on visual content, particularly on the detection of skin color. Although these approaches provide good results, they generally have the disadvantage of a high false positive rate since not all images with large areas of skin exposure are necessarily pornographic images, such as people wearing swimsuits or images related to sports. Local feature based approaches with Bag-of-Words models (BoW) have been successfully applied to visual recognition tasks in the context of pornography detection. Even though existing methods provide promising results, they use local feature descriptors that require a high computational processing time yielding high-dimensional vectors. In this work, we propose an approach for pornography detection based on local binary feature extraction and BossaNova image representation, a BoW model extension that preserves more richly the visual information. Moreover, we propose two approaches for video description based on the combination of mid-level representations namely BossaNova Video Descriptor (BNVD) and BoW Video Descriptor (BoW-VD). The proposed techniques are promising, achieving an accuracy of 92.40 , thus reducing the classification error by 16 over the current state-of-the-art local features approach on the Pornography dataset. We address the problem of describing people based on fine-grained clothing attributes. This is an important problem for many practical applications, such as identifying target suspects or finding missing people based on detailed clothing descriptions in surveillance videos or consumer photos. We approach this problem by first mining clothing images with fine-grained attribute labels from online shopping stores. A large-scale dataset is built with about one million images and fine-detailed attribute sub-categories, such as various shades of color (e.g., watermelon red, rosy red, purplish red), clothing types (e.g., down jacket, denim jacket), and patterns (e.g., thin horizontal stripes, houndstooth). As these images are taken in ideal pose lighting background conditions, it is unreliable to directly use them as training data for attribute prediction in the domain of unconstrained images captured, for example, by mobile phones or surveillance cameras. In order to bridge this gap, we propose a novel double-path deep domain adaptation network to model the data from the two domains jointly. Several alignment cost layers placed inbetween the two columns ensure the consistency of the two domain features and the feasibility to predict unseen attribute categories in one of the domains. Finally, to achieve a working system with automatic human body alignment, we trained an enhanced RCNN-based detector to localize human bodies in images. Our extensive experimental evaluation demonstrates the effectiveness of the proposed approach for describing people based on fine-grained clothing attributes. This paper presents a novel neural network based technique for face detection that eliminates limitations pertaining to the skin color variations among people. We propose to model the skin color in the three dimensional RGB space which is a color cube consisting of all the possible color combinations. Skin samples in images with varying lighting conditions, from the Old Dominion University skin database, are used for obtaining a skin color distribution. The primary color components of each plane of the color cube are fed to a three-layered network, trained using the backpropagation algorithm with the skin samples, to extract the skin regions from the planes and interpolate them so as to provide an optimum decision boundary and hence the positive skin samples for the skin classifier. The use of the color cube eliminates the difficulties of finding the non-skin part of training samples since the interpolated data is consider skin and rest of the color cube is consider non-skin. Subsequent face detection is aided by the color, geometry and motion information analyses of each frame in a video sequence. The performance of the new face detection technique has been tested with real-time data of size 320 spl times 240 frames from video sequences captured by a surveillance camera. It is observed that the network can differentiate skin and non-skin effectively while minimizing false detections to a large extent when compared with the existing techniques. In addition, it is seen that the network is capable of performing face detection in complex lighting and background environments. In certain environments or for certain publics, pornographic content may be considered inappropriate, generating the need to be detected and filtered. Most works regarding pornography detection are based on the detection of human skin. However, a shortcoming of these kind of approaches is related to the high false positive rate in contexts like beach shots or sports. Considering the development of low-level local features and the emergence of mid-level representations, we introduce a new video descriptor, which employs local binary descriptors in conjunction with BossaNova, a recent mid-level representation. Our proposed method outperforms the state-of-the-art on the Pornography dataset.