PT Unknown
AU Sergio Escalera
   Alicia Fornes
   Oriol Pujol
   Petia Radeva
TI Multi-class Binary Symbol Classification with Circular Blurred Shape Models
BT 15th International Conference on Image Analysis and Processing
PY 2009
BP 1005–1014
VL 5716
DI 10.1007/978-3-642-04146-4_107
AB 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.
ER