# Download Analysis of Images, Social Networks and Texts: 4th by Mikhail Yu. Khachay, Natalia Konstantinova, Alexander PDF

By Mikhail Yu. Khachay, Natalia Konstantinova, Alexander Panchenko, Dmitry Ignatov, Valeri G. Labunets

This publication constitutes the court cases of the Fourth overseas 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 geared up in topical sections on research of pictures and video clips; development acceptance and computing device studying; social community research; textual content mining and ordinary 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 resources for Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers

Sample text

The second part is classiﬁer design [16]. ) in this particular case [17]. , CNN) can be applied in this task to select the features [18], the ﬁnal decision is usually done with simple nearest neighbor rule. In view of the small spatial deviations due to misalignment after object detection, the following similarity measure with mutual alignment of blocks and comparison of the histograms in Δ-neighborhood of each block is used [19] qðlÞ ðX; Xr Þ ¼ K ðlÞ X K ðlÞ X k1 ¼1 k2 ¼1   min qH HrðlÞ ðk1 þ D1 ; k2 þ D2 Þ; H ðlÞ ðk1 ; k2 Þ : jD1 j D; jD2 j D ð1Þ Here qH is any distance between HOGs.

We then compared the three models described above based on their coeﬃcient values, p-values for coeﬃcients, and the variance inﬂation factor (VIF) that indicates multicollinearity. All of these metrics are listed in Table 2. Based on Table 2, we distinguish four important points. First of all, in every model the most signiﬁcant variables are the transitivity and community count (regardless of transformation). In every case, the community count has a positive coeﬃcient and transitivity has a negative one.

Hence, their recognition performance is much worse in comparison with conventional non-hierarchical approach. 5–3 times slower than the matching of the state-of-the-art HOGs [3]. , in video-based face recognition [12]. In this paper we propose to perform sequential analysis of query image to overcome this drawback. Namely, more detailed representation of the query image is analyzed at the next level of pyramid only if it is impossible to obtain a reliable solution at the current level [13, 14].