报告题目： Social Multimedia as Sensors

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

　　时 间： 2014年12月19日（周五）14:30-16:00

　　地 点： 三牌楼校区综合科研楼1712室

　　主办单位：通信与信息工程学院、江苏省图像处理与图像通信重点实验室、科技处

　　报告人简介：

　　罗杰波教授目前就职于美国罗彻斯特大学 (University of Rochester, USA) 计算机科学系，是IEEE、SPIE和IAPR等国际著名学会的会士（Fellow），图像处理、计算机视觉、机器学习等领域著名国际学者。罗杰波教授曾于“柯达实验室”从事研究长达十五年，并担任该实验室首席科学家。罗杰波教授是国际顶级会议ACM Multimedia 2010、CVPR 2012大会共同主席，Journal of Multimedia主编，并担任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)等国际顶尖学术期刊编委会成员。罗杰波教授的研究涉及图像处理、计算机视觉、机器学习、数据挖掘、医学影像分析、普适性计算等多个前沿领域，发表超过两百篇学术论文，持有超过七十项美国专利。近年来，罗杰波教授在社交多媒体研究及其社会应用中做出了巨大的贡献。

　　报告摘要：

　　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.