Multimedia Ergonomics in the World of Big Data

点击翻译按钮获取中文摘要

Big Data is redefining our world. Among the various types of data being generated at ever increasing rates, multimedia has been termed "the biggest Big Data" due to its sheer volume, and the ease and pace with which it is being created. From surveillance video, both terrestrial and satellite, through scientific and medical imaging, entertainment, gaming, advertising, and billions of users creating their own content for private and social consumption, multimedia keeps pushing the limits of our technology. For us to make sense of and effectively utilize the information present in this vast universe of multimedia data, new science and engineering will be required. ******The Big Data problem, however, is not new - it has existed in nature for a long time. Our own brains, as well as those of most other animals, had to evolve various strategies to keep up with the large amount of sensory data in order to survive. Since the brain cannot consciously process all the sensory data in real time, selection has to be made as to what is important and what to focus on. And these selections need to be made rather quickly in order to be effective. This is akin to the technological Big Data problem we have today, where the available processing power is miniscule compared to the amount of data generated, and the gap keeps increasing. Questions about what is relevant, important, and deserves our focus are also at the core of the technological Big Data problem. One should therefore expect that useful insights and strategies can be obtained by studying brain's own ways of dealing with this challenge. ******The proposed research program aims to address some of the pressing multimedia Big Data challenges by leveraging on multimedia ergonomics, inspired by the strategies humans have evolved to cope with the streams of their sensory data. Computational attention plays a crucial part in this regard, as it allows one to select a small amount of the most relevant data from a large pool of available data. In recent years, we have applied these principles to the traditional problems of video compression and communications, achieving state of the art results. ******In the proposed program, we will address the multimedia Big Data challenge on two fronts. On the theory side, we will seek a solid scientific underpinning and tractable mathematical models for the computational attention approaches from multiple multimedia sources. These will enable the design of multimedia systems on the Big Data scale. On the application side, we will work to apply the principles of multimedia ergonomics and utilize compressed data representation to develop sophisticated, yet low-cost algorithms for tracking, activity detection and discovery, annotation and retrieval necessary ingredients for making sense of multimedia Big Data.**