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Dominant point detection based on suboptimal feature selection methods
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DOI：10.1016/j.eswa.2020.113741.png)
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Abstract
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This paper presents a viable alternative solution for dominant point detection predicated on the comparison of suboptimal feature selection methods. Suboptimal feature selection methods are utilized as standard criteria to identify dominant points. Considering that all of the combinations of points comprise many sets, an algorithm that eliminates some of them is affirmed and illustrated. The sequential backward selection, sequential forward selection, generalized sequential forward selection, generalized sequential backward selection and plus l-take away r selection methods are performed on the remaining points to extract the dominant points. The simulation results exhibit that this method is significantly more effective and efficient in comparison to other proposed methods. (c) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Dominant point detection
Image compression
Suboptimal feature selection
Turning angle curvature
Computer vision
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