PT Chapter
AU Sergio Escalera
   David M.J. Tax
   Oriol Pujol
   Petia Radeva
   Robert P.W. Duin
TI Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes
BT Innovations in Intelligent Image Analysis
PY 2011
BP 7
EP 29
VL 339
DI 10.1007/978-3-642-17934-1_2
AB A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.
PI Berlin
ER