Learning Object Appearance Models via Transformed Dirichlet Processes

Posted in Conferences, Companies, Science on March 29, 2007


Learning Object Appearance Models via Transformed Dirichlet Processes
Google Tech Talks
March 6, 2007

ABSTRACT

Object recognition systems use features extracted from images to localize and categorize objects. Such methods must be robust to the rich variability of natural scenes, and the often small size of training databases. In this talk, we describe hierarchical generative models for objects, the parts composing them, and the scenes surrounding them. We employ Dirichlet processes to learn flexible appearance models which transfer knowledge among related object categories. By coupling part-based models with spatial transformations, we also consistently account for geometric constraints. Through Monte Carlo methods, we use these transformed Dirichlet processes to categorize objects given few examples, and automatically recognize groups of objects in complex visual scenes.

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