Abstract
Moment invariants are one of the techniques of feature extraction frequently used for pattern recognition algorithms. A moment is a projection of function into polynomial basis and an invariant is a function returning the same value for an input with and without particular class of degradation. Several techniques of moment invariant creation exist often generating over-complete set of invariants. Dependencies in these sets are commonly in a form of complicated polynomials, furthermore they can contain dependencies of higher orders. These theoretical dependencies are valid in the continuous domain but it is well known that in discrete cases are often invalidated by discretization. Therefore, it would be feasible to begin classification with such an over-complete set and adaptively find the pseudo-independent set of invariants by the means of feature selection techniques. This study focuses on testing of the influence of theoretical invariant dependencies in discrete pattern recognition applications.
This work was supported by Czech Science Foundation (GA ČR) under grant GA15-16928S.
This work was supported by The Charles University Grant Agency (GA UK) under grant 1094216.
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References
Flusser, J., Suk, T.: Pattern recognition by affine moment invariants. Pattern Recogn. 26(1), 167–174 (1993)
Suk, T., Flusser, J.: Affine moment invariants generated by graph method. Pattern Recogn. 44(9), 2047–2056 (2011)
Flusser, J., Suk, T., Zitová, B.: 2D and 3D Image Analysis by Moments. Wiley, New York (2016)
Flusser, J., Zitová, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley, New York (2009)
Suk, T., Flusser, J.: Tables of affine moment invariants generated by the graph method. Technical report, Research Report 2156, Institute of Information Theory and Automation (2005)
Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15(11), 1119–1125 (1994)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (2013)
LeCun, Y., Cortes, C., Burges, C.J.: The MNIST database of handwritten digits (1998). http://yann.lecun.com/exdb/mnist. Accessed 7 June 2017
Novotnỳ, P., Suk, T.: Leaf recognition of woody species in Central Europe. Biosyst. Eng. 115(4), 444–452 (2013)
Suk, T., Novotny, P.: Middle European Woods (MEW 2012, 2014) (2014). http://zoi.utia.cas.cz/node/662. Accessed 7 June 2017
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Zita, A., Flusser, J., Suk, T., Kotera, J. (2017). Feature Selection on Affine Moment Invariants in Relation to Known Dependencies. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_24
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DOI: https://doi.org/10.1007/978-3-319-64698-5_24
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