DAYAN MANOHAR SIVALINGAM
Machine Vision Labs, ECE Department, University of Illinois at Chicago, Chicago, Illinois 60607, USA
NARENKUMAR PANDIAN
Machine Vision Labs, ECE Department, University of Illinois at Chicago, Chicago, Illinois 60607, USA
JEZEKIEL BEN-ARIE
Author for correspondence.
ECE Department, M/C 154, University of Illinois at Chicago, 851 S. Morgan St., Chicago, Illinois 60607, USA

In this work, we develop an efficient technique to transform a multiclass recognition problem into a minimal binary classification problem using the Minimal Classification Method (MCM). The MCM requires only log₂ N classifications whereas the other methods require much more. For the classification, we use Support Vector Machine (SVM) based binary classifiers since they have superior generalization performance. Unlike the prevalent one-versus-one strategy (the bottom-up one-versus-one strategy is called tournament method) that separates only two classes at each classification, the binary classifiers in our method have to separate two groups of multiple classes. As a result, the probability of generalization error increases. This problem is alleviated by utilizing error correcting codes, which results only in a marginal increase in the required number of classifications. However, in comparison to the tournament method, our method requires only 50% of the classifications and still similar performance can be attained. The proposed solution is tested with the Columbia Object Image Library (COIL). We also test the performance under conditions of noise and occlusion.

Multiclass recognition; binary classifiers; Minimal Classification Method; Support Vector Machines; object recognition