[1]LIAN Hao,ZENG Xianhua,LI Shufang.Supervised global manifold ranking based image retrieval algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(1):92-97.[doi:10.3969/j.issn.1673-4785.201303021]
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Supervised global manifold ranking based image retrieval algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 1 Page number: 92-97 Column: 学术论文—机器学习 Public date: 2014-02-25
Title:
Supervised global manifold ranking based image retrieval algorithm
Author(s):
LIAN Hao;  ZENG Xianhua;  LI Shufang
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
manifold learning; image retrieval algorithm; manifold ranking; supervised manifold ranking; uncorrelated ranking
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201303021
Abstract:
In manifold ranking based image retrieval, if there is no positive feedback, the situation where the precision ratio is zero can not be changed. So a new supervised global manifold ranking based image retrieval (SG-MRBIR) algorithm is proposed. For improving the image retrieval performance, this algorithm fully applies the local and non-local geometrical distribution of image datasets, image label information and user’s related feedback information. And the related ranking vectors of the retrieved image are corrected by using uncorrelated ranking. Finally, the situation where the first query precision is zero is improved. The experimental results for the MSRA-MM image database show that our SG-MRBIR algorithm distinctly improves the image retrieval performance. Especially, after the first feedback by users in the experiments, the average precision ratio of retrieving was increased as compared with the generalized manifold ranking based image retrieval (G-MRBIR) algorithm. This verifies the effectiveness and superiority of this algorithm.
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