Sciweavers

ICPR
2006
IEEE

Learning Policies for Efficiently Identifying Objects of Many Classes

14 years 4 months ago
Learning Policies for Efficiently Identifying Objects of Many Classes
Viola and Jones (VJ) cascade classification methods have proven to be very successful in detecting objects belonging to a single class -- e.g., faces. This paper addresses the more challenging "many class detection" problem: detecting and identifying objects that belong to any of a set of classes. We use a set of learned weights (corresponding to the parameters of a set of binary linear separators) to identify these objects. We show that objects within many real-world classes tend to form clusters in this induced "classifier space". As the result of a sequence of classifiers can suggest a possible label for each object, we formulate this task as a Markov Decision Process. Our system first uses a "decision tree classifier" (i.e., a policy produced using dynamic programming) to specify when to apply which classifier to produce a possible class label for each sub-image W of a test image. This corresponds to a leaf of the decision tree. It then uses a cascade...
Ahmed M. Elgammal, Ramana Isukapalli, Russell Grei
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Ahmed M. Elgammal, Ramana Isukapalli, Russell Greiner
Comments (0)