Institute of Information Theory and Automation

Publication details

Feature Selection on Affine Moment Invariants in Relation to Known Dependencies

Conference Paper (international conference)

Zita Aleš, Flusser Jan, Suk Tomáš, Kotera Jan

serial: Computer Analysis of Images and Patterns : 17th International Conference, CAIP 2017, p. 285-295 , Eds: Felsberg M., Heyden A., Krüger N.

action: International Conference on Computer Analysis of Images and Patterns (CAIP 2017) /17./, (Ystad, SE, 20170822)

project(s): GA15-16928S, GA ČR, 1094216, GA UK

keywords: Affine invariants, Image moments, Feature selection, Machine learning, Pattern recognition

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abstract (eng):

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, further-\nmore 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 classi cation with such an over-complete\nset and adaptively nd the pseudo-independent set of invariants by the means of feature selection techniques. This study focuses on testing of the infuence of theoretical invariant dependencies in discrete pattern recognition applications.


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Last modification: 21.12.2012
Institute of Information Theory and Automation