Chapter 2 Multi-class Classification in Image Analysis via Error-Correcting Output Codes
Sérgio Escalera, David M. J. Tax, Oriol Pujol, Petia I. Radeva, Robert P. W. Duin · 2011
Abstract. 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 identi-fies 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.