基于分块权值的语义图像检索

计算机科学 ›› 2013, Vol. 40 ›› Issue (9): 266-269.

基于分块权值的语义图像检索

夏利民,朱城,张海燕,彭东亮

中南大学信息科学与工程学院长沙410075;中南大学信息科学与工程学院长沙410075;中南大学信息科学与工程学院长沙410075;中南大学信息科学与工程学院长沙410075

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金(50808025),国家教育部博士点基金(20090162110057)资助

Semantic Image Retrieval Based on Sub-block Weight

XIA Li-min,ZHU Cheng,ZHANG Hai-yan and PENG Dong-liang

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 图像低层视觉特征和高层语义间的“语义鸿沟”是图像检索的关键问题。为了进一步提高基于语义的图像检索系统工作效率,以分块权值和视觉词库为基础,结合图像低层特征和高层语义的相关性,提出了一种基于分块权值的语义图像模型,该模型用来反映图像的视觉特性,对图像的高层语义进行有效检测,从而提高语义图像的检索效率。实验结果表明,该方法提高了语义图像检索系统的查全率和查准率。

关键词: 词袋,图像检索,子块,语义中图法分类号TP391文献标识码A

Abstract: The semantic gap between low-level visual feature and high-level semantic has become a primary problem．For improving the efficiency of semantic-based image retrieval system,this paper based on chunked weight and a visual vocabulary proposed a semantic image model which utilizes the correlation of low-level feature and high-level semantic．The model is used to interpret the image visual characteristic and detecte high-level semantic,which improves the efficiency of the semantic image retrieval．The experimental results show that this method improves the precision and recall of semantic image retrieval system.

Key words: Bags-of-word,Image retrieval,Sub-block,Semantic

夏利民,朱城,张海燕,彭东亮. 基于分块权值的语义图像检索[J]. 计算机科学, 2013, 40(9): 266-269. https://doi.org/

XIA Li-min,ZHU Cheng,ZHANG Hai-yan and PENG Dong-liang. Semantic Image Retrieval Based on Sub-block Weight[J]. Computer Science, 2013, 40(9): 266-269. https://doi.org/

参考文献

[1] Hiremath P S,Pujari J．Content based image retrieval using co-lor,texture and shape features[C]∥Advanced Computing and Communication．Gulbarga:Gulbarga University．2007
[2] 周明全,耿国华,韦娜.基于内容图像检索技术[M]．北京:清华大学出版社,2007
[3] Duygulu P,Barnard K,De Freitas J F G,et al.Object recognition as machine translation:Learning a lexicon for a fixed image vocabulary[J]．Lecture Notes in Computer science,2002(2353):97-112
[4] Spyrou E,Tolias G,Mylonas P,et al．Concept detection and keyframe extraction using a visual thesaurus[J]．Multimedia Tools and Applications,2009,41(3):337-373
[5] Manjunath B,Ohm J,Vasudevan V,et al.Color and texture descriptors[J].IEEE Trans Circuits Syst Video Technol,11(6):703-715
[6] Spyrou E,Tolias G,Mylonas P,et al.Concept detection and key-frame extraction using a visual thesaurus[J]．Multimedia Tools and Applicationgs,2009(41):337-373
[7] Avrithis Y,Doulamis A,Kollias S．A Stochastic Framework for Optimal Key Frame Extraction from MPEG Video Databases[J]．Computer Vision and Image Understanding,1999(75):3-24
[8] Mylonas P,Spyrou E,Avrithis Y,et al.Using Visual Context and Region Semantics for High-Level Concept Detection[J]．IEEE Transaxtions on Multimedia,2009,11(2):229
[9] 曹利华,柳伟,李国辉．基于多种主色调的图像检索算法研究与实现[J]．计算机研究与发展,1999(36):96-100
[10] 张瑜慧．基于SVM的语义图像检索技术的研究与实现[D].扬州:扬州大学,2007
[11] Chang E,Kingshy G,Sychay,et al．CBSA:content-based softannotation for multimodal image retrieval using Bayes point machines[J]．IEEE Transactions on Circuits and Systems for Video Technology,2003(13):26-38
[12] Lowe D G．Object recognition from local scale-invariant features[J].Computer Vision,1999(2):1150-1157
[13] Wang Jun-qiu．Vision-based Global Localization Using a Visual Vocabulary[C]∥Robotics and Automation．Beijing:Peking University,2005

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