Journal of University of Chinese Academy of Sciences >
Remote sensing image retrieval method integrating feature similarity measurement and SVM
Received date: 2012-05-02
Revised date: 2012-05-29
Online published: 2012-05-29
The support vector machine (SVM)-based relevance feedback algorithm has been used in common image retrieval, but not widely applied to remote sensing images. Traditional algorithm only uses SVM classifiers, resulting in some wrong ranking sequences of retrieval results. An improved relevance feedback strategy is proposed, and it modifies the similarity measurement criterion using a weighted linear combination of feature similarity measurement and SVM classifier. Experimental results show that the proposed method improves the ranking sequence and accuracy of retrieval results.
ZHAO Li-Jun , TANG Jia-Kui , YU Xin-Ju , WANG Chun-Lei , ZHANG Cheng-Wen . Remote sensing image retrieval method integrating feature similarity measurement and SVM[J]. Journal of University of Chinese Academy of Sciences, 2013 , 30(3) : 347 -352 . DOI: 10.7523/j.issn.1002-1175.2013.03.011
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