Improving gaussian process classification with outlier detection, with applications in image classification

Abstract

In many computer vision applications for recognition or classification, outlier detection plays an important role as it affects the accuracy and reliability of the result. We propose a novel approach for outlier detection using Gaussian process classification. With this approach, the outlier detection can be integrated to the classification process, instead of being treated separately. Experimental results on handwritten digit image recognition and vision based robot localization show that our approach performs better than other state of the art approaches. © 2011 Springer-Verlag Berlin Heidelberg.