New PDF release: Analysis of Images, Social Networks and Texts: 4th

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

ISBN-10: 3319261223

ISBN-13: 9783319261225

ISBN-10: 3319261231

ISBN-13: 9783319261232

This ebook constitutes the lawsuits 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 one hundred forty submissions. The papers are prepared in topical sections on research of pictures and movies; trend reputation and desktop studying; social community research; textual content mining and common language processing.

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Additional info for Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers

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Therefore nowadays it is a norm to build grammars for normalization of non-standard words by hand. For example, the developers of a commercial speech synthesis system VitalVoice apply a partial morphological and syntactic analysis for normalization of NSWs and detection of the correct stress position in Russian words [8]. In [2] the English normalization module employs heuristic disambiguation and expansion rules. Reichel et al. 9 % word error rate in normalization of English cardinal and ordinal numbers by finite state transducers, spelling unknown abbreviations and pronouncing unknown acronyms as standard words if the latter do not violate phonotactics.

In the second case, K ð1Þ ¼ 10, K ð2Þ ¼ 15 and K ð3Þ ¼ 20. Weights in (4) were found experimentally to obtain the higher accuracy. We evaluate the error rate (in %) and the average time (in ms) to recognize one test image with a modern laptop (4 core i7, 6 Gb RAM) and Visual C++ 2013 compiler and optimization by speed. We use multithreading to make brute-force search (1), (5) faster. Each thread is implemented with Windows ThreadPool API and operates only on a subset of the database. , we look for the nearest neighbor (5) in 8 parallel threads.

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.

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Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers by Mikhail Yu. Khachay, Natalia Konstantinova, Alexander Panchenko, Dmitry Ignatov, Valeri G. Labunets


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