Computer Science ›› 2017, Vol. 44 ›› Issue (7): 315-317, 323.doi: 10.11896/j.issn.1002-137X.2017.07.057

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Research on Unsupervised Regional Remote Sensing Image Retrieval Algorithm Based on Graph Theory

LI Li-ping, ZHAO Chuan-rong, KONG De-ren and WANG Fang   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to improve the content based remote sensing image retrieval technology,a new image retrieval algorithm based on graph theory was proposed.First,the proposed method models each image by a graph and combines local information and related spatial structures,which provides the region based image representation.Each image is initially divided into different regions.The nodes and boundaries of the attribute relation graph respectively represent the regionalfeatures and the spatial relations between them.Then,image retrieval is achieved based on image similarity.To match the corresponding image and realize image retrieval according to image similarities,a new type of non-accurate image matching strategy is used based on sub-graph isomorphism algorithm and spectral graph embedding technology.The experimental results show that compared with the most advanced unsupervised remote sensing image retrieval methods,the retrieval performance of the proposed method is significantly improved.

Key words: Image retrieval,Graph theory,Unsupervised learning,Attribute relation graph(ARG),Sub-graph isomorphism

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