CAREER: Categorization and Identification of Visual Scenes

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CAREER: Categorization and Identification of Visual ScenesPI: Aude OlivaOne remarkable aspect of visual recognition is that humans are able to recognize the meaning (or "gist") of complex visual scenes within 1/20 of a second, independently of the quantity of objects in the scene. This rapid understanding phenomenon can be experienced while looking at rapid sequences in television advertisements and quick cuts in modern movie trailers. How is this remarkable feat accomplished? Research over the last decade has made substantial progress toward understanding the mechanisms underlying single object recognition, but less progress has been made toward understanding scene recognition. For example, computer systems fall well short of human performance in tasks that require recognizing the gist of a scene. Dr. Aude Oliva has undertaken a novel approach to this challenging question by studying mechanisms of analysis that are global in nature, focusing on statistically robust features describing the spatial layout of the scene (e.g. its volume, its perspective, its level of clutter) and not merely its components (e.g., the objects in a scene). With National Science Foundation support, Dr. Aude Oliva will conduct a five-year CAREER award study to examine how a global approach to image analysis can explain humans remarkable ability to recognize scenes and objects. Moreover she will use this approach to define operational strategies for machine vision systems. This program of research will combine a number of methodologies, including behavioral experiments (psychophysics, eye tracking), cognitive neuroscience methods (event-related potentials), and computational modeling. Applications of this work might include scene and space recognition systems to assist drivers, automatic systems that could provide semantic descriptions of the contents of large image databases, and computer assisted systems to aid the visually-impaired in navigating through visual space. The educational mission proposed by Dr. Oliva includes laboratory training of graduate and undergraduate students in the cognitive and computational methods of scene understanding, as well as a new course on computational visual cognition, and a winter tutorial together with an annual symposium both on scene understanding.