Exploring Entity Resolution for Multimedia Person Identification

OF THE DISSERTATION Exploring Entity Resolution for Multimedia Person Identification By Liyan Zhang Doctor of Philosophy in Computer Science University of California, Irvine, 2014 Professor Sharad Mehrotra, Chair The explosion of massive media data induced by the proliferation of digital cameras, mobile devices as well as the emergence of online media websites, has led us into the era of big data. Accurate and effective analyses of the big multimedia data to support semantically enriched representation in terms of events, activities, and entities can bring transformative improvements to a variety of application domains. The basic form of multimedia analysis for more sophisticated interpretation is characterized by questions such as “who, what, where, when” that identify subjects, activities, locations, and time associated with images/video segments. In this thesis, we primarily focus on the “who” question, which is referred as the person identification problem in multimedia data. While advances in image processing and computer vision has resulted in powerful techniques for person identification, such techniques based on the facial appearance representations, are usually prone to errors due to a variety of factors including noise, poor signal quality, occlusion, etc. It is widely recognized in the multimedia research community that additional contextual features can be leveraged to bring significant improvements to such tasks. Nevertheless, how to systematically utilize the heterogeneous contextual information still poses a big challenge. Besides, the person identification procedure is conventionally processed in an “offline” setting where the typical goal is to achieve complete annotations of the whole