A Bayesian Framework For Image Recognition Based On Hidden Markov Eigen-Image Models

An image recognition method based on hidden Markov eigen-image models (HMEMs) using a Bayesian framework is proposed and experimentally evaluated. HMEMs have been proposed as a model with two advantageous properties: invariances to the size and location of the object to be recognized, and a linear feature extraction based on statistical analysis. However, the training of HMEMs easily falls into the overfitting problem because HMEMs have a complex model structure, especially when there is insufficient training data. This study aims to accurately estimate HMEMs using the variational Bayesian (VB) method. The VB method can utilize prior distributions representing useful prior information and is expected to have a high generalization ability due to the marginalization of model parameters. Furthermore, to relax the local maximum problem in the VB method, the deterministic annealing expectation maximization algorithm is applied to train HMEMs. Experiments on face recognition indicate that the proposed method offers a significantly improved image recognition performance. Additionally, comparative experimental results show that the proposed method is more robust to geometric variations than convolutional neural networks when the amount of training data is insufficient. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.