Bayesian classification and active learning using lp-priors. Application to image segmentation
Pablo Ruiz, Nicolás Pérez de la Blanca, Rafael Molina, Aggelos K. Katsaggelos · 2014
In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classifi-cation and Active Learning. For p < 1, lp-priors are used to impose sparsity on the adaptive parameters. Using variational inference, all model parameters are estimated and the posterior probabilities of the classes given the samples are calculated. A relationship between the prior model used and the independent Gaussian prior model is provided. The posterior probabilities are used to classify new sam-ples and to define two Active Learning methods to improve classifier performance: Minimum Probability and Maximum Entropy. In the experimental section the proposed Bayesian framework is applied to Image Segmentation problems on both synthetic and real datasets, showing higher accuracy than state-of-the-art approaches. 1.