By Mikhail Yu. Khachay, Natalia Konstantinova, Alexander Panchenko, Dmitry Ignatov, Valeri G. Labunets
This ebook constitutes the complaints of the Fourth foreign convention on research of pictures, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015.
The 24 complete and eight brief papers have been rigorously reviewed and chosen from a hundred and forty submissions. The papers are equipped in topical sections on research of pictures and video clips; development reputation and desktop studying; social community research; textual content mining and normal language processing.
Read Online or Download Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers PDF
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Extra info for Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers
AUC: a statistically consistent and more discriminating measure than accuracy. Proc. Int. Joint Conf. Artif. Intel. 2003, 519–526 (2003) Sequential Hierarchical Image Recognition Based on the Pyramid Histograms of Oriented Gradients with Small Samples Andrey V. Savchenko1(&), Vladimir R. Milov2, and Natalya S. a. E. ru Abstract. 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.
Ru 6 Steklov Mathematical Institute at St. Petersburg, St. com Abstract. In online social networks, high level features of user behavior such as character traits can be predicted with data from user proﬁles and their connections. Recent publications use data from online social networks to detect people with depression propensity and diagnosis. In this study, we investigate the capabilities of previously published methods and metrics applied to the Russian online social network VKontakte. com and performed comparative analysis between them and randomly sampled users.
886–893 (2005) 4. : SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008) 5. : Delving Deep into Rectiﬁers: Surpassing Human-Level Performance on ImageNet Classiﬁcation. org/abs/1502. 01852 (2015) 6. : On Intelligence. Times Books, New York (2004) Sequential Hierarchical Image Recognition 23 7. : Stacked hierarchical labeling. , Paragios, N. ) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 57–70. Springer, Heidelberg (2010) 8. : Representing shape with a spatial pyramid kernel.