Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICASSP.2009.4959711
Title: Directed Markov stationary features for visual classification
Authors: Ni, B.
Yan, S. 
Kassim, A. 
Keywords: Directed Markov stationary features
Markov stationary features
Visual classification
Issue Date: 2009
Citation: Ni, B., Yan, S., Kassim, A. (2009). Directed Markov stationary features for visual classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings : 825-828. ScholarBank@NUS Repository. https://doi.org/10.1109/ICASSP.2009.4959711
Abstract: We investigate how to effectively incorporate spatial structure information into histogram features for boosting visual classification performance motivated by recently proposed Markov Stationary Features (MSF). First, we show that due to the symmetric property of the image occurrence modeling procedure, the stationary distribution derived from the normalized co-occurrence matrix has a trivial informative solution which only approximates the original histogram representation, i.e., does not encode proper spatial structure information. To eliminate this ambiguity, we propose in this work the so called Directed Markov Stationary Features (DMSF) to encode spatial information into histogram features, and the asymmetric essence of the co-occurrence matrices in DMSF avoids the trivial informative solutions in MSF. Extensive experiments on face recognition show the significant performance improvement brought by our proposed DMSF. ©2009 IEEE.
Source Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/69966
ISBN: 9781424423545
ISSN: 15206149
DOI: 10.1109/ICASSP.2009.4959711
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