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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Multi-class Binary Symbol Classification with Circular Blurred Shape Models
verfasst von : Sergio Escalera, Alicia Fornés, Oriol Pujol, Petia Radeva
Erschienen in: Image Analysis and Processing – ICIAP 2009
Verlag: Springer Berlin Heidelberg
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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-of-the-art descriptors, showing significant performance improvements.