Abstract
This paper develops two techniques of oriented texture analysis: the modified Gabor filters (MGF) and the Gaussian Markov random field model with circular neighborhoods (CGMRF). The neighborhood-oscillating tabu search algorithm (NOTS) is proposed to solve the MGF/CGMRF feature fusion problem, and compared with classical algorithms, such as sequential forward selection and sequential forward floating selection methods. Based on the experimental results, NOTS is shown to be a promising tool for feature fusion, and the MGF/CGMRF fused features achieved by NOTS perform better than either MGF or CGMRF alone according to the Fisher criterion and classification accuracy.
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© 2005 International Federation for Information Processing
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Zhao, Y., Zhang, L., Li, P. (2005). Feature Fusion with Neighborhood-Oscillating Tabu Search for Oriented Texture Classification. In: Li, D., Wang, B. (eds) Artificial Intelligence Applications and Innovations. AIAI 2005. IFIP — The International Federation for Information Processing, vol 187. Springer, Boston, MA. https://doi.org/10.1007/0-387-29295-0_73
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DOI: https://doi.org/10.1007/0-387-29295-0_73
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-28318-0
Online ISBN: 978-0-387-29295-3
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