Knowledge Commons of Institute of Automation,CAS
Multi-Modal Supervised Latent Dirichlet Allocation for Event Classification in Social Media | |
Shengsheng Qian; Tianzhu Zhang; Changsheng Xu | |
2014-07 | |
会议名称 | International Conference on Internet Multimedia Computing and Service |
会议日期 | July 10-12, 2014 |
会议地点 | Xiamen, China |
摘要 | In social media, many existing websites (e.g., Flickr, YouTube, and Facebook) are for users to share their own interests and opinions of many popular events, and successfully facilitate the event generation, sharing and propagation. As a result, there are substantial amounts of user-contributed media data (e.g., images, videos, and textual content) for a wide variety of real-world events of different types and scales. The aim of this paper is to automatically identify the interesting events from massive social media data, which are useful to browse, search and monitor social events by users or governments. To achieve this goal, we propose a novel multi-modal supervised latent dirichlet allocation (mm-SLDA) for social event classification. Our proposed mm-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the multi-class property of social events jointly. (2) It makes use of the supervised social event category label information and is able to classify multi-class social event directly. We evaluate our proposed mm-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14792 |
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 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
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. Multi-Modal Supervised Latent Dirichlet Allocation for Event Classification in Social Media[C],2014. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICIMCS14_MMSLDA for (1198KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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