Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 89, Issue -, Pages 362-373Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2017.08.009
Keywords
Shape analysis; Complex networks; Computer vision; Feature extraction; Classification
Categories
Funding
- Fundect [071/2015]
- CNPq [444985/2014-0, CNPq 134558/2016-2]
- Fapesp [2016/02557-0]
- programs PET - Fronteira
- NERDS da Fronteira
Ask authors/readers for more resources
We introduce a method for shape recognition based on the angular analysis of Complex Networks. Our method models shapes as Complex Networks defining a more descriptive representation of the inner angularity of the shape's perimeter. The result is a set of measures that better describe shapes if compared to previous approaches that use only the vertices' degree. We extract the angle between the Complex Network edges, and then we analyze their distribution along with a network dynamic evolution. The proposed approach, named Angular Descriptors of Complex Networks (ADCN), presents a high discriminatory power, as evidenced by experiments conducted in five datasets. It is rotation invariant, presents high robustness against scale changes and degradation levels, overcoming traditional methods such as Zernike moments, Multiscale Fractal dimension, Fourier, Curvature and the degree-based descriptors of Complex Networks. (C) 2017 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available