Hidden Markov Model Based 2D Shape Classification
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.
Unable to display preview. Download preview PDF.
- 4.Gao, J., Kosaka, A., Kak, A.: Interactive color image segmentation editor driven by active contour model. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 245–249 (1999)Google Scholar
- 7.McDermott, E.: Handbook of Neural Networks for speech processing, ch. 5, pp. 159–216. Artech House (2000)Google Scholar