Title :
Multilayer Scene Similarity Assessment
Author :
Stefanidis, Anthony ; Wang, Caixia ; Xu, Lu ; Curtin, Kevin M.
Author_Institution :
Dept. of Geogr. & Geoinformation Sci., George Mason Univ., Fairfax, VA, USA
Abstract :
As we move increasingly towards multi-source data analysis, the assessment of similarity of complex, multilayer scenes is becoming increasingly important for spatial data mining. In this paper, we present a content-based approach for scene similarity assessment. The proposed approach is based on a graph-matching scheme that models linear feature networks (road network) as graphs and additional GIS information (e.g. buildings) as layer content. This allows us to combine diverse but co-located pieces of information (e.g. roads and buildings) in an integrated similarity assessment process. In the paper we present key theoretical concepts and provide experimental results to demonstrate the capability and robustness of the proposed approach.
Keywords :
content-based retrieval; data analysis; data mining; geographic information systems; geophysics computing; graph theory; information analysis; visual databases; GIS information; content-based approach; graph-matching scheme; linear feature networks; multi-source data analysis; multilayer scene similarity assessment; spatial data mining; Conferences; Data analysis; Data mining; Geographic Information Systems; Geography; Layout; Network topology; Nonhomogeneous media; Roads; USA Councils; graph; road network; scene query; similarity;
Conference_Titel :
Data Mining Workshops, 2009. ICDMW '09. IEEE International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-5384-9
Electronic_ISBN :
978-0-7695-3902-7
DOI :
10.1109/ICDMW.2009.117