Angular descriptors of complex networks: A novel approach for boundary shape analysis

• Published in: Expert systems with applications Vol. 89; pp. 362 - 373
• Main Authors: Scabini, Leonardo F.S., Fistarol, Danilo O., Cantero, Sávio V., Gonçalves, Wesley N., Machado, Bruno Brandoli, Rodrigues, Jr, Jose F.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2017; Elsevier BV
• Subjects: Classification; Complex networks; Computer vision; Curvature; Experiments; Feature extraction; Graph theory; Multiscale analysis; Networks; Recognition; Shape analysis; Shape recognition; Computer vision; Feature extraction; Shape analysis; Complex networks; Classification
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.08.009

•A new method for shape analysis based on angular measures of complex networks.•The angle between complex network connections is calculated and its occurrence is evaluated.•Dynamic approach for complex network analysis with automatic threshold selection. 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.