Hidden Markov Model Based Weighted Likelihood Discriminant for Minimum Error Shape Classification

The goal of this communication is to present a weighted likelihood discriminant for minimum error shape classification. Different from traditional maximum likelihood (ML) methods in which classification is carried out based on probabilities from independent individual class models as is the case for general hidden Markov model (HMM) methods, our proposed method utilizes information from all classes to minimize classification error. Proposed approach uses a hidden Markov model as a curvature feature based 2D shape descriptor. In this contribution we present a generalized probabilistic descent (GPD) method to weight the curvature likelihoods to achieve a discriminant function with minimum classification error. In contrast with other approaches, a weighted likelihood discriminant function is introduced. We believe that our sound theory based implementation reduces classification error by combining hidden Markov model with generalized probabilistic descent theory. We show comparative results obtained with our approach and classic maximum-likelihood calculation for fighter planes in terms of classification accuracies