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A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

by 1, 2 and 1,*
1
Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming 650500, China
2
School of Information, Yunnan Normal University, Kunming 650500, Yunnan, China
*
Author to whom correspondence should be addressed.
Algorithms 2018, 11(7), 97; https://doi.org/10.3390/a11070097
Received: 8 May 2018 / Revised: 14 June 2018 / Accepted: 21 June 2018 / Published: 1 July 2018
(This article belongs to the Special Issue Discrete Algorithms and Discrete Problems in Machine Intelligence)
The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection. View Full-Text
Keywords: video mining; topic model; inference algorithm; anomaly detection video mining; topic model; inference algorithm; anomaly detection
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Tang, L.; Liu, L.; Gan, J. A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining. Algorithms 2018, 11, 97.

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