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Community detection is one of the most important problems in social network analysis in the context of the structure of underlying graphs. Many researchers have proposed methods, which only consider the network structure of social networks, for discovering dense regions in social networks. However, increasing media information in networks, such as images, videos, user tags, and comments, are observed...
We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera...
The use of clustering systems is very important in those real-word applications where an efficient, both accurate and economical, representation of the data to be processed is necessary. When dealing with statistical models, such a problem is usually related to the estimate of their parameters in the Maximum Likelihood context. At this regard, we propose an EM-based algorithm that uses a hierarchical...
Nonnegative matrix tri-factorization (NMTF) is a 3-factor decomposition of a nonnegative data matrix, X ap USVT, where factor matrices, U, S, and V , are restricted to be nonnegative as well. Motivated by the aspect model used for dyadic data analysis as well as in probabilistic latent semantic analysis (PLSA), we present a probabilistic model with two dependent latent variables for NMTF, referred...
Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks generated for region clusters, using low level features are matched with words in various ways. In this work, we ensured that clustering is more meaningful by using words in associated text in addition to image data in clustering of image regions to generate a codebook. We first compute topic...
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