Next Article in Journal
Measurement of Potential Victims of Burglary at the Mesoscale: Comparison of Census, Phone Users, and Social Media Data
Previous Article in Journal
Deep Understanding of Urban Dynamics from Imprint Urban Toponymic Data Using a Spatial–Temporal–Semantic Analysis Approach
Article

Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors

1
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3
School of Geographical Science and Urban Planning, Arizona State University, Tempe, AZ 875302, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Suzana Dragicevic and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(5), 279; https://doi.org/10.3390/ijgi10050279
Received: 15 February 2021 / Revised: 30 March 2021 / Accepted: 26 April 2021 / Published: 28 April 2021
In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor. View Full-Text
Keywords: shape similarity; grid context; texture shape similarity; grid context; texture
Show Figures

Figure 1

MDPI and ACS Style

Fan, H.; Zhao, Z.; Li, W. Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors. ISPRS Int. J. Geo-Inf. 2021, 10, 279. https://doi.org/10.3390/ijgi10050279

AMA Style

Fan H, Zhao Z, Li W. Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors. ISPRS International Journal of Geo-Information. 2021; 10(5):279. https://doi.org/10.3390/ijgi10050279

Chicago/Turabian Style

Fan, Hongchao, Zhiyao Zhao, and Wenwen Li. 2021. "Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors" ISPRS International Journal of Geo-Information 10, no. 5: 279. https://doi.org/10.3390/ijgi10050279

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop