Relevance feedback based on genetic programming for image retrieval

被引：54

作者：

Ferreira, C. D. ^{[1
]}

Santos, J. A. ^{[1
]}

Torres, R. da S. ^{[1
]}

Goncalves, M. A. ^{[2
]}

Rezende, R. C. ^{[1
]}

Fan, Weiguo ^{[3
]}

机构：

[1] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP, Brazil

[2] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG, Brazil

[3] Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA

来源：

PATTERN RECOGNITION LETTERS | 2011年 / 32卷 / 01期

基金：

巴西圣保罗研究基金会;

关键词：

Relevance feedback; Content-based image retrieval; Genetic programming; DESCRIPTOR; DISCOVERY; FRAMEWORK;

D O I：

10.1016/j.patrec.2010.05.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant. Several experiments were conducted to validate the proposed frameworks. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed frameworks were compared, and outperformed six other relevance feedback methods regarding their effectiveness and efficiency in image retrieval tasks. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：27 / 37

页数：11

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