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2009
ACM

Multi-class Binary Symbol Classification with Circular Blurred Shape Models

12 years 6 months ago
Multi-class Binary Symbol Classification with Circular Blurred Shape Models
Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-ofthe-art descriptors, showing significant performance improvements.
Sergio Escalera, Alicia Fornés, Oriol Pujol
Added 24 Nov 2009
Updated 24 Nov 2009
Type Conference
Year 2009
Where ICIAP
Authors Sergio Escalera, Alicia Fornés, Oriol Pujol, Petia Radeva
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