https://scholars.lib.ntu.edu.tw/handle/123456789/413017
標題: | Unsupervised latent aspect discovery for diverse event summarization | 作者: | Lee W.-Y. Kuo Y.-H. Hsieh P.-J. Cheng W.-F. Chao T.-H. Hsieh H.-L. Tsai C.-E. Chang H.-C. Lan J.-S. WINSTON HSU |
關鍵字: | Event summarization; Multimodal; Visualization | 公開日期: | 2015 | 起(迄)頁: | 197-200 | 來源出版物: | 2015 ACM Multimedia Conference | 摘要: | Recently, the fast growth of social media communities and mobile devices encourages more people to share their media data online than ever before. Analyzing data and summarizing data into useful information have become increasingly popular and important for modern society. Given a set of event keywords and a dataset, this paper performs event summarization, aiming to discover and summarize what people may concern for each event from the given dataset. More specifically, this paper extracts latent sub-events with diverse and representative attributes for each given event. This paper proposes effective methods on detecting events with (1) human attribute discovery, such as human pose and clothes, (2) scene analysis, (3) image aspect discovery, and (4) temporal and semantic analysis, to provide people different perspectives for the events they are interested in. For practical implementation, this paper studied and conducted experiments on YFCC100M, which is a dataset with 100 million of photos and videos, provided by Yahoo!. Finally, a comprehensive and complete system is created accordingly to support diverse event summarization. ? 2015 ACM. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413017 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962802490&doi=10.1145%2f2733373.2809935&partnerID=40&md5=ec8d9bd67b075d7611124afaa324e9b5 |
ISBN: | 9781450334594 | DOI: | 10.1145/2733373.2809935 | SDG/關鍵字: | Flow visualization; Mobile devices; Aspect discoveries; Complete system; Event summarization; Human attributes; Multi-modal; Scene analysis; Semantic analysis; Set of events; Semantics |
顯示於: | 資訊工程學系 |
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