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Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation | |
Shengsheng Qian1,2![]() ![]() ![]() | |
发表期刊 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
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2014-12-01 | |
卷号 | 11期号:2页码:1-22 |
文章类型 | Article |
摘要 | With the rapidly increasing popularity of social media sites (e.g., Flickr, YouTube, and Facebook), it is convenient for users to share their own comments on many social events, which successfully facilitates social event generation, sharing and propagation and results in a large amount of user-contributed media data (e.g., images, videos, and text) for a wide variety of real-world events of different types and scales. As a consequence, it has become more and more difficult to exactly find the interesting events from massive social media data, which is useful to browse, search and monitor social events by users or governments. To deal with these issues, we propose a novel boosted multimodal supervised Latent Dirichlet Allocation (BMM-SLDA) for social event classification by integrating a supervised topic model, denoted as multi-modal supervised Latent Dirichlet Allocation (mm-SLDA), in the boosting framework. Our proposed BMM-SLDA has a number of advantages. (1) Our mm-SLDA can effectively exploit the multimodality and the multiclass property of social events jointly, and make use of the supervised category label information to classify multiclass social event directly. (2) It is suitable for large-scale data analysis by utilizing boosting weighted sampling strategy to iteratively select a small subset of data to efficiently train the corresponding topic models. (3) It effectively exploits social event structure by the document weight distribution with classification error and can iteratively learn new topic model to correct the previously misclassified event documents. We evaluate our BMM-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods. |
关键词 | Algorithms Experimentation Performance Social Event Classification Multimodality Supervised Lda Adaboost Social Media |
WOS标题词 | Science & Technology ; Technology |
关键词[WOS] | RECOGNITION ; ANNOTATION |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods |
WOS记录号 | WOS:000348308800004 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/2822 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Changsheng Xu |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.China Singapore Inst Digital Media, Singapore 119613, Singapore 3.King Saud Univ, Coll Comp & Informat Sci, SWE Dept, Riyadh, Saudi Arabia |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Shengsheng Qian,Tianzhu Zhang,Changsheng Xu,et al. Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2014,11(2):1-22. |
APA | Shengsheng Qian,Tianzhu Zhang,Changsheng Xu,&M. Shamim Hossain.(2014).Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,11(2),1-22. |
MLA | Shengsheng Qian,et al."Social Event Classification via Boosted Multimodal Supervised Latent Dirichlet Allocation".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 11.2(2014):1-22. |
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TOMM2014_Social Even(6388KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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