Unsupervised Learning of Models for Object Recognition

Monday, November 13, 2000 - 11:00am - 12:00pm
Keller 3-180
Pietro Perona (California Institute of Technology)
Recognizing objects in images is one of the most important functions of our visual system. Not only can we recognize individual objects, such as the Eiffel Tower or our grandmothers face, but also categories of objects, such as shoes, automobiles and frogs. Considerable attention has been devoted to formulating models and algorithms that may explain visual recognition; however, no theory is yet available for how these models may be trained automatically in realistic conditions: Can a child, or a machine, learn to recognize `faces and `cars only by looking? This is at best a difficult task: everyday images are cluttered and may not contain explicit information on the presence, location and structure of new objects. I will present a computational theory of how object models may be learned from such data. Object categories are modelled as collections of parts that appear in a characteristic spatial arrangement. Both part appearance and constellation shape are modelled probabilisitcally. Model training is achieved by maximum likelyhood.