Computer Studies Bldg. 209
Increasingly rich and large-scale image related data are being posted to social network and media sharing websites. Researchers from multidisciplinary areas, including machine learning, computer vision, data mining, and human machine interaction, are developing methods for employing such multi-modality data for various applications. We present several recent advances in this arena of opportunities and challenges. First, we address the multi-modality feature issue by developing new machinery called Heterogeneous Feature Machines (HFM), which builds a kernel logistic regression model based on similarities that combine different features and distance metrics with group LASSO constraints. Its power is demonstrated across a wide variety of visual recognition tasks including scene, event, and action recognition. Second, we examine the recently popular data driven approach that has seemingly diminished the need for machine learning in favor of simply relying on large scale data. We believe it is important to address crucial machine learning issues, in particular cross-domain learning, in order to intelligently leverage large scale web data to solve problems such as searching personal images by keywords and recognizing events in personal videos. Next, we explore the global trends and sentiments that can be drawn by mining the sharing patterns of uploaded and downloaded social multimedia. We consider that each time an image or video is uploaded or shared, it constitutes an implicit yet trustworthy vote for (or against) the subject of the image. By aggregating such votes across millions of Internet users, we reveal the wisdom that is embedded in social multimedia sites for prediction and forecast in politics, economics, and marketing. Finally, we briefly discuss an algorithm for interactive co-segmentation of a foreground object from a group of related images. This algorithm can intelligently recommend, through active learning, where the user should scribble next in what image, and enable users to quickly achieve good quality cutouts.
Bio: Jiebo Luo is a Senior Principal Scientist with the Kodak Research Laboratories in Rochester, NY. His research interests include image processing, computer vision, machine learning, social media data mining, medical imaging, and computational photography. Dr. Luo has authored over 160 technical papers and holds over 60 US patents. Dr. Luo has been actively involved in numerous technical conferences, including serving as the general chair of ACM CIVR 2008, program co-chair of IEEE CVPR 2012 and ACM Multimedia 2010, area chair of IEEE ICASSP 2009-2011, ICIP 2008-2011, CVPR 2008 and ICCV 2011, and an organizer of ICME 2006/2008/2010 and ICIP 2002. Currently, he serves on several IEEE Technical Committees (IMDSP, MMSP, and MLSP) and conference steering committees (ACM ICMR and IEEE ICME). He is the Editor-in-Chief of the Journal of Multimedia, and has served on the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), the IEEE Transactions on Multimedia (TMM), the IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Pattern Recognition (PR), Machine Vision and Applications (MVA), and Journal of Electronic Imaging (JEI). He is a Fellow of the SPIE, IEEE, and IAPR.
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