Stationary features and cat detection

Tuesday, October 6, 2009 - 10:45am - 11:15am
Lind 305
Donald Geman (Johns Hopkins University)
Keywords: object
detection, invariant features, hierarchical search

This talk is about research in scene interpretation. Most algorithms
for detecting and describing instances from object categories consist
of looping over a partition of a pose space with dedicated binary
classifiers. This strategy is inefficient for a complex pose:
fragmenting the training data severely reduces accuracy, and the
computational cost is prohibitive due to visiting a massive pose
partition. To overcome data-fragmentation I will discuss a novel
framework centered on pose-indexed features, which allows for
efficient, one-shot learning of pose-specific classifiers. Such
features are designed so that the probability distribution of the
response is invariant if an object is actually present. I will
illustrate these ideas by detecting and localizing cats in highly
cluttered greyscale scenes. This is joint work with Francois Fleuret.
MSC Code: