Modelling and classification of shapes in two-dimensions using vector quantization

Simon Lee, Brian C. Lovell

Abstract: This paper is an extension of work by He and Kundu (1991) in which the task of object recognition is performed on silhouettes or outlines. He and Kundu reduce a 2-dimensional image to a 1-dimensional segment sequence and use the autoregressive (AR) model for feature extraction and hidden Markov model (HMM) for classification. We show that due to the AR model's inability to estimate abrupt changes (i.e. where the signal is not bandlimited), poor image modelling is obtained. By direct application of vector quantization (VQ) to the normalized data segments, the image features are retained better than with AR modelling. Furthermore, by replacing the HMM classification with VQ distortion, better recognition results are obtained with reduced training times as compared to the HMM algorithm.<