Ou Wu

Recognition of Pornographic Web Pages by Classifying Texts and Images

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the objects contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

Learning to predict the perceived visual quality of photos

Visual quality (VisQ) representation is a fundamental step in the learning of a VisQ prediction model for photos. It not only reflects how we understand VisQ but also determines the label type. Existing studies apply a scalar value (i.e., a categorical label or a score) to represent VisQ. As VisQ is a subjective property, only a scalar value is insufficient to represent humans perceived VisQ of a photo. This study represents VisQ by a distribution on pre-defined ordinal basic ratings in order to capture the subjectivity of VisQ better. When using the new representation, the label type is structural instead of scalar. Conventional learning algorithms cannot be directly applied in model learning. Meanwhile, for many photos, the numbers of users involved in the evaluation are limited, making some labels unreliable. In this study, a new algorithm called support vector distribution regression (SVDR) is presented to deal with the structural output learning. Two independent learning strategies (reliability-sensitive learning and label refinement) are proposed to alleviate the difficulty of insufficient involved users for rating. Combining SVDR with the two learning strategies, two separate structural-output regression algorithms (i.e., reliability-sensitive SVDR and label refinement-based SVDR) are produced. Experimental results demonstrate the effectiveness of our introduced learning strategies and learning algorithms.

Efficient Clustering Aggregation Based on Data Fragments

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Topic Detection and Tracking for Threaded Discussion Communities

The threaded discussion communities are one of the most common forms of online communities, which are becoming more and more popular among web users. Everyday a huge amount of new discussions are added to these communities, which are difficult to summarize and search. In this paper, we propose a topic detection and tracking (TDT) method for the discussion threads. Most existing TDT methods deal with the news stories, but the language used in discussion data are much more casual, oral and informal compared with news data. To solve this problem, we design several extensions to the basic TDT framework, focusing on the very nature of discussion data, including a thread/post activity validation step, a term pos-weighting strategy, and a two-level decision framework considering not only the content similarity but also the user activity information. Experiment results show that our pro-posed method greatly improves current TDT methods in real discussion community environment. The discussion data can be better organized for searching and visualization with the help of TDT.

Recognition of blue movies by fusion of audio and video

Along with the explosive growth of the Internet, comes the proliferation of pornography. Compared with the pornographic texts and images, blue movies can do much harm to children, due to the greater realism and voyeurism of blue movies. In this paper, a framework for recognizing blue movies by fusing the audio and video information is described. A one-class Gaussian mixture model (GMM) is used to recognize porno-sounds. A generalized contour-based pornographic image recognition algorithm is used to detect pornographic image frames of a video shot. Then a fusion algorithm based on the Bayes theory is employed to combine the recognition results from audio and video. Experimental results demonstrate that our framework which exploits both audio and video modalities is more robust and achieves better performance than one which uses either one alone.

Patch-based skin color detection and its application to pornography image filtering

Along with the explosive growth of the World Wide Web, an immense industry for the production and consumption of pornography has grown. Though the censorship and legal restraints on pornography are discriminating in different historical, cultural and national contexts, selling pornography to minors is not allowed in most cases. Detecting human skin tone is of utmost importance in pornography image filtering algorithms. In this paper, we propose two patch-based skin color detection algorithms: regular patch and irregular patch skin color detection algorithms. On the basis of skin detection, we extract 31-dimensional features from the input image, and these features are fed into a random forest classifier. Our algorithm has been incorporated into an adult-content filtering infrastructure, and is now in active use for preventing minors from accessing pornographic images via mobile phones.

A Novel Web Page Filtering System by Combining Texts and Images

With the rapid development of the Internet, people benefit much from the sharing of information. Meanwhile, the WWW era is a double-edged sword which spreads harmful and erotic content widely. In this paper, a new statistical approach has been exploited by combining the results of two or more different classification methods using our filtering system. We first briefly introduce the classification of discrete texts, continuous texts and images separately, and then describe the specific way we have been exploring to merge the text and image classification result. Also there is a section illustrating our system framework. Finally we assess our method by demonstrating the experimental results and comparing it to some common-used filtering methods

Measuring the Visual Complexities of Web Pages

Visual complexities (VisComs) of Web pages significantly affect user experience, and automatic evaluation can facilitate a large number of Web-based applications. The construction of a model for measuring the VisComs of Web pages requires the extraction of typical features and learning based on labeled Web pages. However, as far as the authors are aware, little headway has been made on measuring VisCom in Web mining and machine learning. The present article provides a new approach combining Web mining techniques and machine learning algorithms for measuring the VisComs of Web pages. The structure of a Web page is first analyzed, and the layout is then extracted. Using a Web page as a semistructured image, three classes of features are extracted to construct a feature vector. The feature vector is fed into a learned measuring function to calculate the VisCom of the page. In the proposed approach of the present study, the type of the measuring function and its learning depend on the quantification strategy for VisCom. Aside from using a category and a score to represent VisCom as existing work, this study presents a new strategy utilizing a distribution to quantify the VisCom of a Web page. Empirical evaluation suggests the effectiveness of the proposed approach in terms of both features and learning algorithms.

Horror video scene recognition via Multiple-Instance learning

Along with the ever-growing Web comes the proliferation of objectionable content, such as pornography, violence, horror information, etc. Horror videos, whose threat to childrens health is no less than pornographic video, are sometimes neglected by existing Web filtering tools. Consequently, an effective horror video filtering tool is necessary for preventing children from accessing these harmful horror videos. In this paper, by introducing color emotion and color harmony theories, we propose a horror video scenes recognition algorithm. Firstly, the video scenes are decomposed into a set of shots. Then we extract the visual features, audio features and emotional features of each shot, the video scene is viewed as a bag and each shot is treated as an instance of the corresponding bag. Finally, by combining the three features, the horror video scenes are recognized by the Multiple-Instance learning(MIL). According to the experimental results on diverse video scenes, the proposed scheme based on the emotional perception could effectively deal with the horror video scene recognition and promising results are achieved.