Shape classification using Hidden Markov Model and structural feature

Bangwang Xie; Zhiyong Wang; Jiajun Wang

doi:10.1117/12.743168

15 November 2007 Shape classification using hidden Markov model and structural feature

Bangwang Xie, Zhiyong Wang, Jiajun Wang

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880C (2007) https://doi.org/10.1117/12.743168
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

A novel shape classification method based on Hidden Markov Models (HMMs) is proposed in the paper. Instead of characterizing points along an object contour, our method employs HMMs to model the relationship among structural segments of the contour. Firstly, an object contour is partitioned into segments at points with zero curvature value. Secondly, each segment is represented with structural features. Finally, a HMMs is utilized to characterize the object contour by treating each segment as an observation of a hidden state. Promising experimental results obtained on two popular shape datasets demonstrate that the proposed method is efficient in classifying shapes, particularly unclosed shapes and similar shapes.

Citation Download Citation

Bangwang Xie, Zhiyong Wang, and Jiajun Wang "Shape classification using hidden Markov model and structural feature", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880C (15 November 2007); https://doi.org/10.1117/12.743168

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