Multi-class Binary Symbol Classification with Circular Blurred Shape Models

  • Authors:

  • Sergio Escalera;Alicia Fornés;Oriol Pujol;Petia Radeva

  • Affiliations:

  • Computer Vision Center, Bellaterra, Spain 08193 and Dept. Matemàtica Aplicada i Anàlisi, UB, Barcelona, Spain 08007;Computer Vision Center, Bellaterra, Spain 08193 and Dept. Computer Science, Bellaterra, Spain 08193;Computer Vision Center, Bellaterra, Spain 08193 and Dept. Matemàtica Aplicada i Anàlisi, UB, Barcelona, Spain 08007;Computer Vision Center, Bellaterra, Spain 08193 and Dept. Matemàtica Aplicada i Anàlisi, UB, Barcelona, Spain 08007

  • Venue:
  • ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
  • Year:
  • 2009

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Abstract

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.