Shape Classifer Based on Generalized Probabilistic Descent Method with Hidden Markov Descriptor

Abstract

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 2D shape descriptor. In this contribution, we introduce a weighted likelihood discriminant function and present a minimum error classification strategy based on generalized probabilistic descent (GPD) method. We believe our sound theory based implementation reduces classification error by combining HMM with GPD theory. We show comparative results obtained with our approach and classic ML classification along with Fourier descriptor and Zernike moments based classification for fighter planes and vehicle shapes.