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Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects (1996)
| Content Provider | CiteSeerX |
|---|---|
| Author | Viola, Paul A. |
| Description | AI Memo 1591, Massachusetts Institute of Technology |
| Abstract | We have developed a new Bayesian framework for visual object recognition which is based on the insight that images of objects can be modeled as a conjunction of local features. This framework can be used to both derive an object recognition algorithm and an algorithm for learning the features themselves. The overall approach, called complex feature recognition or CFR, is unique for several reasons: it is broadly applicable to a wide range of object types, it makes constructing object models easy, it is capable of identifying either the class or the identity of an object, and it is computationally efficient -- requiring time proportional to the size of the image. Instead of a single simple feature such as an edge, CFR uses a large set of complex features that are learned from experience with model objects. The response of a single complex feature contains much more class information than does a single edge. This significantly reduces the number of possible correspondences between the mo... |
| File Format | |
| Publisher Date | 1996-01-01 |
| Access Restriction | Open |
| Subject Keyword | Several Reason Model Object Complex Feature Possible Correspondence Wide Range Overall Approach Class Information Single Complex Feature New Bayesian Framework Local Feature Single Edge Object Model Large Set Single Simple Feature Object Type Bayesian Approach Complex Feature Recognition Visual Object Recognition Object Recognition Algorithm |
| Content Type | Text |
