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CBIR用户查询模式及系统构建

  • 师文
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  • 南京大学信息管理学院多媒体信息处理研究所
师文,南京大学信息管理学院多媒体信息处理研究所博士研究生,E-mail:wens163@163.com。

收稿日期: 2014-02-17

  修回日期: 2014-03-05

  网络出版日期: 2014-03-20

基金资助

本文系国家社会科学基金重大项目“图书、博物、档案数字化服务融合研究”(项目编号:10&ZD134)研究成果之一。

Use Query Schema and System Construction for CBIR

  • Shi Wen
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  • Institute of Multimedia Information Processing, School of Information Management, Nanjing University, Nanjing 210093

Received date: 2014-02-17

  Revised date: 2014-03-05

  Online published: 2014-03-20

摘要

分析CBIR系统的用户查询模式以及基于形状特征的检索系统构建相关技术,在系统构建中应用示例图像与采样图像两种方式对图像的形状特征进行检索,通过图像分割获取目标轮廓,利用轮廓点与兴趣点之间的空间分布关系构造形状描述函数,应用傅立叶变换提取图像特征,最后在系统检索实验中证明其有效性。

本文引用格式

师文 . CBIR用户查询模式及系统构建[J]. 图书情报工作, 2014 , 58(06) : 118 -122 . DOI: 10.13266/j.issn.0252-3116.2014.06.020

Abstract

In this paper, the user query schema of CBIR is discussed, and the technology of shape-based retrieval system is analyzed. Two query ways using example and sample image are used in the experiment system construction. Image segmentation technology is used to acquire the target contour. In the shape description step, the spatial relationship between contour points and salient points are established by the relation function, and the image features are extracted by using Fourier transform. Experiments with a combined dataset witness the effectiveness of the system, which has certain reference significance to the research of relevant technologies in digital libraries.

参考文献

[1] 朱学芳. 数字图像信息资源开发及管理[J].中国图书馆学报,2002(6):36-38.

[2] Premachandran V, Kakarala R. Perceptually motivated shape context which uses shape interiors[J]. Pattern Recognition,2013,46(8): 2092-2102.

[3] Kwitt R, Meerwald P, Uhl A. Efficient texture image retrieval using copulas in a Bayesian framework[J]. IEEE Transactions on Image Processing,2011,20 (7):2063-2077.

[4] Liu Guanghai, Li Zuo Yong, Zhanglei, et al. Image retrieval based on micro-structure descriptor[J]. Pattern Recognition,2011,44(9):2123-2133.

[5] Arnold W M S, Marcel W, Simone S, et al. Content-based image retrieval at the end of the early years[J]. IEEE Transactions of Pattern Analysis and Machine Intelligence,2000,22(12):1349-1380.

[6] Aptoula E, Lefèvre S. Morphological description of color images for content-based image retrieval[J]. IEEE Transactions on Image Processing,2009,18 (11):2505-2517.

[7] Datta R, Joshi D,Li Jia, et al. Image retrieval:Ideas, influences, and trends of the new age[J]. ACM Computing Surveys,2008,40(2):1-60.

[8] Van De Sande K E A, Gevers T, Snoek C G M. Evaluating color descriptors for object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(9):1582-1596.

[9] Manjunath B S, Ma W Y. Texture features for browsing and retrieval of image data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):837-842.

[10] Wang Bin.Shape retrieval using combined Fourier features[J]. Optics Communications,2011,284(14):3504-3508.

[11] Lowe D G. Object recognition from local scale-invariant features[C/OL]//Proceedings of IEEE International Conference on Computer Vision, 1992:1150-1157.[2014-03-02].http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=790410.

[12] Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors[J]. International Journal of Computer Cision, 2004, 60(1):63-86.

[13] 王斌. 一种用于形状描述的拱高半径复函数[J]. 电子学报, 2011,39(4):831-836.

[14] Shu Xin, Wu Xiaojun. A novel contour descriptor for 2D shape matching and its application to image retrieval[J]. Image and Vision Computing, 2011,29(4): 286-294.

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