10.1184/R1/6468500.v1 Filipe Condessa Filipe Condessa Jose Bioucas-Dias Jose Bioucas-Dias Carlos A. Castro Carlos A. Castro John A. Ozolek John A. Ozolek Jelena Kovacevic Jelena Kovacevic Classification with reject option using contextual information Carnegie Mellon University 2013 image classification reject option discriminative random fields 2013-04-01 00:00:00 Journal contribution https://kilthub.cmu.edu/articles/journal_contribution/Classification_with_reject_option_using_contextual_information/6468500 <p>We propose a new algorithm for classification that merges classification with reject option with classification using contextual information. A reject option is desired in many image-classification applications requiring a robust classifier and when the need for high classification accuracy surpasses the need to classify the entire image. Moreover, our algorithm improves the classifier performance by including local and nonlocal contextual information, at the expense of rejecting a fraction of the samples. As a probabilistic model, we adopt a multinomial logistic regression. We use a discriminative random model for the description of the problem; we introduce reject option into the classification problem through association potential, and contextual information through interaction potential. We validate the method on the images of H&E-stained teratoma tissues and show the increase in the classifier performance when rejecting part of the assigned class labels.</p>