  We introduce a new supervised algorithm for image classification with
rejection using multiscale contextual information. Rejection is desired in
image-classification applications that require a robust classifier but not the
classification of the entire image. The proposed algorithm combines local and
multiscale contextual information with rejection, improving the classification
performance. As a probabilistic model for classification, we adopt a
multinomial logistic regression. The concept of rejection with contextual
information is implemented by modeling the classification problem as an energy
minimization problem over a graph representing local and multiscale
similarities of the image. The rejection is introduced through an energy data
term associated with the classification risk and the contextual information
through an energy smoothness term associated with the local and multiscale
similarities within the image. We illustrate the proposed method on the
classification of images of H&E-stained teratoma tissues.
