Abstract
Currently, most of the feature extraction methods based on micro-patterns are application oriented. The micro-patterns are intuitively user-designed based on experience. Few works have built models of micro-patterns for feature extraction. In this paper, we propose a model-based feature extraction approach, which uses micro-structure modeling to design adaptive micro-patterns. We first model the micro-structure of the image by Markov random field. Then we give the generalized definition of micro-pattern based on the model. After that, we define the fitness function and compute the fitness index to encode the image’s local fitness to micro-patterns. Theoretical analysis and experimental results show that the new algorithm is both flexible and effective in extracting good features.
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Yang, Q., Gong, D., Tang, X. (2005). Modeling Micro-patterns for Feature Extraction. In: Zhao, W., Gong, S., Tang, X. (eds) Analysis and Modelling of Faces and Gestures. AMFG 2005. Lecture Notes in Computer Science, vol 3723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564386_2
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DOI: https://doi.org/10.1007/11564386_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29229-6
Online ISBN: 978-3-540-32074-6
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