2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Models with Spectral Features for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Neural Networks for Speech Processing
Handbook of Neural Networks for Speech Processing
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiscale representation method for nonrigid shapes with a single closed contour
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a novel two step shape classification approach consisting of a description and a discrimination phase. In the description phase, curvature features are extracted from the shape and are utilized to build a Hidden Markov Model (HMM). The HMM provides a robust Maximum Likelihood (ML) description of the shape. In the discrimination phase, a weighted likelihood discriminant function is formulated, which weights the likelihoods of curvature at individual points of shape to minimize the classification error. The weighting scheme emulates feature selection procedure in which features important for classification are selected. A Generalized Probabilistic Descent (GPD) method based method for estimation of the weights is proposed. To demonstrate the accuracy of the proposed method, we present classification results achieved for fighter planes in terms of classification accuracy and discriminant functions.