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Contextual Priming for Object Detection

Author(s)
Torralba, Antonio; Sinha, Pawan
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Abstract
There is general consensus that context can be a rich source of information about an object's identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin to those that apply to a single object. Here we introduce a simple probabilistic framework for modeling the relationship between context and object properties based on the correlation between the statistics of low-level features across the entire scene and the objects that it contains. The resulting scheme serves as an effective procedure for object priming, context driven focus of attention and automatic scale-selection on real-world scenes.
Date issued
2001-09-01
URI
http://hdl.handle.net/1721.1/7239
Other identifiers
AIM-2001-020
CBCL-205
Series/Report no.
AIM-2001-020CBCL-205
Keywords
AI, context, image statistics, Bayesian reasoning, recognition, focus of attention

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