IEICE TRANSACTIONS on Information
Sequential Bayesian Nonparametric Multimodal Topic Models for Video Data Analysis
Summary :
Topic modeling as a well-known method is widely applied for not only text data mining but also multimedia data analysis such as video data analysis. However, existing models cannot adequately handle time dependency and multimodal data modeling for video data that generally contain image information and speech information. In this paper, we therefore propose a novel topic model, sequential symmetric correspondence hierarchical Dirichlet processes (Seq-Sym-cHDP) extended from sequential conditionally independent hierarchical Dirichlet processes (Seq-CI-HDP) and sequential correspondence hierarchical Dirichlet processes (Seq-cHDP), to improve the multimodal data modeling mechanism via controlling the pivot assignments with a latent variable. An inference scheme for Seq-Sym-cHDP based on a posterior representation sampler is also developed in this work. We finally demonstrate that our model outperforms other baseline models via experiments.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.4 pp.1079-1087
- Publication Date
- 2018/04/01
- Publicized
- 2018/01/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017DAP0021
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Jianfei XUE
Kobe University
Koji EGUCHI
Kobe University
Keyword
Latest Issue
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Jianfei XUE, Koji EGUCHI, "Sequential Bayesian Nonparametric Multimodal Topic Models for Video Data Analysis" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 4, pp. 1079-1087, April 2018, doi: 10.1587/transinf.2017DAP0021.
Abstract: Topic modeling as a well-known method is widely applied for not only text data mining but also multimedia data analysis such as video data analysis. However, existing models cannot adequately handle time dependency and multimodal data modeling for video data that generally contain image information and speech information. In this paper, we therefore propose a novel topic model, sequential symmetric correspondence hierarchical Dirichlet processes (Seq-Sym-cHDP) extended from sequential conditionally independent hierarchical Dirichlet processes (Seq-CI-HDP) and sequential correspondence hierarchical Dirichlet processes (Seq-cHDP), to improve the multimodal data modeling mechanism via controlling the pivot assignments with a latent variable. An inference scheme for Seq-Sym-cHDP based on a posterior representation sampler is also developed in this work. We finally demonstrate that our model outperforms other baseline models via experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017DAP0021/_p
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@ARTICLE{e101-d_4_1079,
author={Jianfei XUE, Koji EGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Sequential Bayesian Nonparametric Multimodal Topic Models for Video Data Analysis},
year={2018},
volume={E101-D},
number={4},
pages={1079-1087},
abstract={Topic modeling as a well-known method is widely applied for not only text data mining but also multimedia data analysis such as video data analysis. However, existing models cannot adequately handle time dependency and multimodal data modeling for video data that generally contain image information and speech information. In this paper, we therefore propose a novel topic model, sequential symmetric correspondence hierarchical Dirichlet processes (Seq-Sym-cHDP) extended from sequential conditionally independent hierarchical Dirichlet processes (Seq-CI-HDP) and sequential correspondence hierarchical Dirichlet processes (Seq-cHDP), to improve the multimodal data modeling mechanism via controlling the pivot assignments with a latent variable. An inference scheme for Seq-Sym-cHDP based on a posterior representation sampler is also developed in this work. We finally demonstrate that our model outperforms other baseline models via experiments.},
keywords={},
doi={10.1587/transinf.2017DAP0021},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Sequential Bayesian Nonparametric Multimodal Topic Models for Video Data Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 1079
EP - 1087
AU - Jianfei XUE
AU - Koji EGUCHI
PY - 2018
DO - 10.1587/transinf.2017DAP0021
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2018
AB - Topic modeling as a well-known method is widely applied for not only text data mining but also multimedia data analysis such as video data analysis. However, existing models cannot adequately handle time dependency and multimodal data modeling for video data that generally contain image information and speech information. In this paper, we therefore propose a novel topic model, sequential symmetric correspondence hierarchical Dirichlet processes (Seq-Sym-cHDP) extended from sequential conditionally independent hierarchical Dirichlet processes (Seq-CI-HDP) and sequential correspondence hierarchical Dirichlet processes (Seq-cHDP), to improve the multimodal data modeling mechanism via controlling the pivot assignments with a latent variable. An inference scheme for Seq-Sym-cHDP based on a posterior representation sampler is also developed in this work. We finally demonstrate that our model outperforms other baseline models via experiments.
ER -
English
