Analysis of Images, Social Networks and Texts. 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers
This book constitutes the proceedings of the Fourth International Conference on Analysis of Images, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015. The 24 full and 8 short papers were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on analysis of images and videos; pattern recognition and machine learning; social network analysis; text mining and natural language processing.
In this paper we explore an application of the pyramid HOG (Histograms of Oriented Gradients) features in image recognition problem with small samples. A sequential analysis is used to improve the performance of hierarchical methods. We propose to process the next, more detailed level of pyramid only if the decision at the current level is unreliable. The Chow’s reject option of comparison of the posterior probability with a fixed threshold is used to verify recognition reliability. The posterior probability is estimated for the homogeneity-testing probabilistic neural network classifier on the basis of its relation with the Bayesian decision. Experimental results in face recognition are presented. It is shown that the proposed approach allows to increase the recognition performance in 2–4 times in comparison with conventional classification of pyramid HOGs.
The mechanisms of real-world social network formation and evolution are one of the most important topics in the field of network science. In this study we collect data about the development of the Vkontakte (a popular Russian social networking site) network of first-year students at a Russian university. We analyze the network formation process from the moment of network establishing until its stabilization. Using Conditional Uniform Graph Test, we compare the graph-level indices of the observed network with random same-size networks that were generated according to random, preferential attachment, and small-world algorithms. We propose two explanatory mechanisms of online network growth: the connected component attachment mechanism and the brokerage mechanism.