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Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
February 2004 (vol. 26 no. 2)
pp. 281-286
Abstract—In this paper, Hidden Markov Models (HMMs) are investigated for the purpose of classifying planar shapes represented by their curvature coefficients. In the training phase, special attention is devoted to the initialization and model selection issues, which make the learning phase particularly effective. The results of tests on different data sets show that the proposed system is able to accurately classify objects that were translated, rotated, occluded, or deformed by shearing, also in the presence of noise.
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Index Terms:
Hidden Markov Models, 2D shape classification, model selection, probabilistic learning.
Citation:
Manuele Bicego, Vittorio Murino, "Investigating Hidden Markov Models' Capabilities in 2D Shape Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 281-286, Jan. 2004, doi:10.1109/TPAMI.2004.1262200
