@InProceedings{SergioEscalera2009,
author="Sergio Escalera
and Alicia Fornes
and Oriol Pujol
and Petia Radeva",
title="Multi-class Binary Symbol Classification with Circular Blurred Shape Models",
booktitle="15th International Conference on Image Analysis and Processing",
year="2009",
publisher="Springer Berlin Heidelberg",
volume="5716",
pages="1005--1014",
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.",
optnote="MILAB;HuPBA;DAG",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1186), last updated on Tue, 17 Dec 2013 15:28:23 +0100",
isbn="978-3-642-04145-7",
issn="0302-9743",
doi="10.1007/978-3-642-04146-4_107"
}