This paper addresses video inter-intra similarity retrieval for pornographic classification. The main approaching method is obtaining the internal representation and external between a single unlabeled batches of labeled videos, then combining together to determine its label. For representation, we extracted inner features within frames clustered them find representative centroid as intra-featu...

Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. In this context, we recently proposed GESL (Bellet et al., 2012), an approach to string edit similarity learning based on loss minimization which offers theo...

The main aim of this paper is to analyze the method of Principal Component Analysis (PCA) and its performance when applied to face recognition. In previous work [10], they use Euclidian Distance for recognition. Here we use Neural Network and Random Forest Classifier which has much higher recognition accuracy than other methods. This algorithm creates a subspace (face space) where the faces in ...

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such...

We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image s...

A modified movable object tracking algorithm which uses the flexible Metric Distance Transform kernel and FCM Classifier is proposed and tested. The target shape which defines the dn Distance Transform is found based on conventional statistical parameters as feature vector extraction and Fuzzy C-Mean (FCM) classifier to differentiate tracked target from background. This replaces the more usual ...

A watershed-based segmentation approach that uses iterative bilateral smoothing with an adapting photometric similarity parameter for pre-filtering is adopted for the segmentation of rock scenes. But the resultant segmented images also contain non-rock watershed regions which are not desired for measuring rock sizes. A proximity-based classifier is applied for the removal of the latter using fe...

In many classification problems, overlapping classes and outliers can significantly decrease a classifier performance. In this paper, we introduce the possibility of a given classifier to reject patterns either for ambiguity or for distance. From a set of typicality degrees for a pattern to be classified, we propose to use fuzzy implications to quantify the similarity of the degrees. A class-se...

Modern medical diagnosis systems very often accumulate numerous data. These computer medical support systems are used in monitoring and diagnosis, which require the various tools to interpret captured parameters. In the paper the new type of the computer medical support system has been presented. The system uses diagnostic conclusion method based on a similarity between the patient’s condition ...

— Topic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the classifier uses (and is trained with) selected terms from the input texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the inp...