Describing Visual Scenes Using Transformed Objects and Parts |
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Authors: | Erik B Sudderth Antonio Torralba William T Freeman Alan S Willsky |
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Affiliation: | (1) Computer Science Division, University of California, Berkeley, USA;(2) Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA |
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Abstract: | We develop hierarchical, probabilistic models for objects, the parts composing them, and the visual scenes surrounding them.
Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently
accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better
exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among
object categories improves detection accuracy when learning from few examples. Turning to multiple object scenes, we propose
nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. The resulting transformed Dirichlet process (TDP)
leads to Monte Carlo algorithms which simultaneously segment and recognize objects in street and office scenes. |
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Keywords: | Object recognition Dirichlet process Hierarchical Dirichlet process Transformation Context Graphical models Scene analysis |
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