Hidden Markov Model-Based Weighted Likelihood Discriminant for 2-D Shape Classification

The goal of this paper is to present a weighted likelihood discriminant for minimum error shape classification. Different from traditional maximum likelihood (ML) methods, in which classification is based on probabilities from independent individual class models as is the case for general hidden Markov model (HMM) methods, proposed method utilizes information from all classes to minimize classification error. The proposed approach uses a HMM for shape curvature as its 2-D shape descriptor. We introduce a weighted likelihood discriminant function and present a minimum classification error strategy based on generalized probabilistic descent method. We show comparative results obtained with our approach and classic ML classification with various HMM topologies alongside Fourier descriptor and Zernike moments-based support vector machine classification for a variety of shapes.