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　　報告題目： Social Multimedia as Sensors

　　報告人： 羅杰波(Jiebo Luo) 教授，Fellow of the IEEE, SPIE and IAPR

　　時 間： 2014年12月19日（周五）14:30-16:00

　　地 點： 三牌樓校區綜合科研樓1712室

　　主辦單位：通信與信息工程學院、江蘇省圖像處理與圖像通信重點實驗室、科技處

　　報告人簡介：

　　羅杰波教授目前就職于美國羅徹斯特大學 (University of Rochester, USA) 計算機科學系，是IEEE、SPIE和IAPR等國際著名學會的會士（Fellow），圖像處理、計算機視覺、機器學習等領域著名國際學者。羅杰波教授曾于“柯達實驗室”從事研究長達十五年，并擔任該實驗室首席科學家。羅杰波教授是國際頂級會議ACM Multimedia 2010、CVPR 2012大會共同主席，Journal of Multimedia主編，并擔任IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI)、IEEE Transactions on Multimedia(TMM)、IEEE Transactions on Circuits and Systems for Video Technology(CSVT)、Pattern Recognition(PR)、Machine Vision and Applications(MVA)和Journal of Electronic Imaging(JEI)等國際頂尖學術期刊編委會成員。羅杰波教授的研究涉及圖像處理、計算機視覺、機器學習、數據挖掘、醫學影像分析、普適性計算等多個前沿領域，發表超過兩百篇學術論文，持有超過七十項美國專利。近年來，羅杰波教授在社交多媒體研究及其社會應用中做出了巨大的貢獻。

　　報告摘要：

　　Increasingly rich and large-scale social multimedia data (including text, images, audio, video) are being generated and posted to social networking and media sharing websites. Researchers from multidisciplinary areas are developing methods for processing social multimedia and employing such rich multi-modality data for various applications. We present a few recent advances in the area of using social multimedia as sensors. Specifically, this tutorial consists of two parts. The first part is on sensing users from heterogeneous, complex, and dynamic social multimedia. We will introduce four elements in the loop of sensing users' user profile, context, multi-modal input, and interactivity. In particular, we will address estimation of user profile, accurate and comprehensive estimation of a mobile user's geo-context from phone-captured photos, and personalized mobile recommendation based on context information. The second part is about sensing social activities from user-generated social multimedia contents, including suggesting suitable social groups from a user's personal photo collection, producing popular and diverse tourism routes from crowd-sourced geo-tagged photos, extracting user sentiment from both textual and visual information in social media, and forecasting election outcome based on image sharing activities and image sentiments. In addition, we will also share interesting findings regarding cultural differences in social multimedia between US and China, as well as thoughts on current challenges and future directions.