Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier

Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier

Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier

Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier

Adjacent Zero Communication Parallel Cloud Computing Method and Its System for N-Body Problem with Short-Range Interaction Domain Decomposition

Although parallel computing is used in the existing numerical solutions of N-body problem, tons of communications between N particles render the parallel efficiency extremely low. Despite the fact that domain decomposition based on short-range interaction is used, when N is exceedingly large and lots of communications exist between particles in adjacent areas, the parallel efficiency remains terribly low. This paper puts forward adjacent zero communication parallel cloud computing method for N-body problem with short-range interaction domain decomposition. According to this method, the adjacent subblock data are exchanged and redundantly stored without acquiring data from other subblocks in the parallel processing, so the waiting time for data transmission can be saved and hence the parallel processing efficiency can be enhanced substantially

An Efficient Lip-reading Method Using K-nearest Neighbor Algorithm

Many studies have been carried out on lip reading, most of those works are based on color images, while some essential features might not be obtained, like inner lip information. In this paper, RGB-D camera will be introduced for improving the recognition rate of lip reading. We try to complete lip reading through using only gray-scale images. Thirteen groups of words are given, and we present eight features for classification. Volunteers are asked to sit in the front of RGB-D camera. For each word we select 15 frames. K-nearest neighbor algorithm (KNN) is used to select the same words between different volunteers. DOI: http://dx.doi.org/10.11591/telkomnika.v13i1.6872

Authorship Attribution with Topic Drift Model

Authorship attribution is an active research direction due to its legal and financial importance. The goal is to identify the authorship of anonymous texts. In this paper, we propose a Topic Drift Model (TDM), monitoring the dynamicity of authors’ writing style and latent topics of interest. Our model is sensitive to the temporal information and the ordering of words, thus it extracts more information from texts

visualizing the random forest by 3d techniques

Random forest which contains a set of decision trees is a popular method in data mining. It has the advantages of high accuracy, high learning speed and the ability of dealing with high dimensional data. The decision model from the training process, however, is non-deterministic because of the sampling process. Although we can calculate correlations between different decision trees to infer the performance, it's not comprehensive to non-specialists. So, the goal of this project is to find a way of visualizing the learning process and the final model using 3D techniques. As a consequence, it can help in model selection by visualizing the patterns of different trees in terms of density, similarity and so on. Moreover, it can help users to understand how rules are learnt and then applied in decision making. Finally, it can provide an interactive interface for manual modifications (e.g. pruning). © 2012 Springer-Verlag.Random forest which contains a set of decision trees is a popular method in data mining. It has the advantages of high accuracy, high learning speed and the ability of dealing with high dimensional data. The decision model from the training process, however, is non-deterministic because of the sampling process. Although we can calculate correlations between different decision trees to infer the performance, it's not comprehensive to non-specialists. So, the goal of this project is to find a way of visualizing the learning process and the final model using 3D techniques. As a consequence, it can help in model selection by visualizing the patterns of different trees in terms of density, similarity and so on. Moreover, it can help users to understand how rules are learnt and then applied in decision making. Finally, it can provide an interactive interface for manual modifications (e.g. pruning). © 2012 Springer-Verlag

Redar: A Remote Desktop Architecture for the Distributed Virtual Personal Computing”, National Research Center for Intelligent Computing Systems

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