期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

以能量和峰度为特征的轮廓波纹理检索算法 被引量：3

Sub-band Energy and Kurtosis Featured Contourlet Texture Retrieval Algorithm
下载PDF
导出
摘要 轮廓波纹理图像检索系统检索率较低的一个重要原因在于纹理特征的提取.针对该问题,从数学角度分析了纹理图像在轮廓波变换域上的统计特性,证明了采用子带系数的能量和峰度作为特征能够更好地刻画纹理结构.在此理论基础上,构造了一种纹理图像检索算法.该算法采用子带系数的能量和峰度级联来构造特征向量,并采用Canberra距离作为相似度度量函数.实验结果表明:在特征向量长度、检索时间、所需存储空间相同的情况下,该检索算法比基于同样架构的已有轮廓波检索算法有更高的检索率. One important reason of low retrieval rate of contourlet texture image retrieval system dues to the texture feature extraction. Focus on this problem, the statistical character of texture image in contourlet domain sub-bands co- efficients were analyzed from mathematical view, energy and kurtosis were proved to be the most suitable features for charaetering textures. According to this theory, a texture image retrieval algorithm was proposed. The algorithm cas- cades the energy and kurtosis of each sub-band in contourlet domain to form feature vectors and Canberra distance for similarity measurement. Experiments results showed the proposed image retrieval algorithm is superior to that of the original contourlet system with same length of feature vectors, retrieval time and memory.
作者 陈新武 马文娟
机构地区 信阳师范学院物理电子工程学院
出处 《信阳师范学院学报（自然科学版）》 CAS 北大核心 2014年第3期432-435,共4页 Journal of Xinyang Normal University(Natural Science Edition)
基金 国家自然科学基金项目(60572048) 河南省教育厅科学技术研究重点项目(12A510020) 信阳师范学院2013年度大学生科研基金项目(2013-DXS-109)
关键词 纹理图像检索 轮廓波变换 检索率 峰度 texture image retrieval contourlet transform retrieval rate kurtosis
分类号 TN911.7 [电子电信—通信与信息系统]
  • 相关文献

参考文献13

  • 1Datta R, Joshi D, Li J, et al. Image retrieval : Ideas, influences, and trends of the new age [ J ]. ACM Computing Surveys ,2008,40 ( 2 ) : 1-60.
  • 2Zhang L, Wang L, Lin W, et al. Geometric optimum experimental design for collaborative image retrieval [ J ]. Circuits and Systems lor Video Technology, IEEE Transactions on,2014, 24 (2) : 346-359.
  • 3Golzalez-Diaz I, Baz-Hormigos C E, Diaz-de-Maria F. A generative model for concurrent image retrieval and ROI segmentation [ J ]. IEEE Transactions on Multimedia ,2014,16 ( 1 ) : 169-183.
  • 4Singha M, ttemachandran K, Paul A. Content-based image retrieval using the combination of the fast wavelet transformation and the colour histo- gram[J]. IET Image Processing,2012, 6(9) : 1221-1226.
  • 5Atto A M, Berthoumieu Y, Bolon P. 2-D wavelet packet spectrum for texture analysis[ J]. IEEE Transactions on Image Processin, 2013,22 ( 6 ) : 2495-2500.
  • 6Lu Y, Do M N. A new contourlet transform with sharp frequency localization [ C ]//Proc of IEEE International Conference on Image Processing, Atlanta, USA, 2006: 8-1 1.
  • 7Cunha D, Zhou J, Do M. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Process- ing, 2006, 15(10) : 3089-3101.
  • 8An C, Oraintara S, Nguyen T. Using phase and magnitude information of the complex directional filter bank for texture image retrieval [ C ]// Proceedings of IEEE International Conference on Image Processing, Texas, USA, 2007:61-64.
  • 9陈新武,龚俊斌,刘玮,田金文.基于映射的复轮廓波变换纹理图像检索系统[J].系统工程与电子技术,2009,31(12):2982-2987. 被引量：2
  • 10Kokare "M, Chatterji B N, Biswas P K. Comparison of similarity metrics for texture image retrieval[ C ]//Proceedings of IEEE TENCON Confer- ence, Bangalore, India ,2003:571-575.

二级参考文献17

  • 1Do M N,Vetterli M.Contourlets :A directional multiresolution image representation[C]//Proceedings International Conference on Image Processing, 2002, 1 : 357-360.
  • 2Lu Y,Do M N.CRISP-Contourlets:A critically sampled directional multiresolution image representation[C]//Proc of SPIE Conference on Wavelet Applications in Signal and Image Processing,San Diego, USA,2003 : 10-21.
  • 3Butt P J,Adelson E H.The laplacian pyramid as a compact image code[J].IEEE Trans Commun, 1983,31 (4) :532-540.
  • 4Bamberger R H,Smith M.A filter bank for the directional decomposition of images:Theory and design[J].IEEE Trans Signal PROC, 1992,40 ( 4 ) : 882 -893.
  • 5Kokare M,Biswas P K,Chatterji B N.Texture image retrieval using rotated wavelet fihers[J].Pattern Recognition Letters, 2007,28 (10) : 1240-1249.
  • 6Kokare M,Chatterji B N,Biswas P K.Comparison of similarity metrics for texture image retrieval[C]//Proceedings of IEEE TENCON Conference, 2003 : 571-575.
  • 7Randen T.Brodatz texture images [EB/OL].http://www.ux.uis.no/- tranden/brodatz.html.
  • 8Smeulders A,Worring M,Santini S,et al.Content-based image retrieval at the end of the early years[J].IEEE Trans Pattern Recognit Machine Intell,2000(22): 1349-1380.
  • 9Vetterli M.Wavelet-based texture retrieval using Generalized gaussian density and kullback-leibler distance[J].IEEE Transactions on Image Processing, 2002,11 (2) : 146-158.
  • 10Laine A,Fan J.Texture classification by wavelet packet signatures[J]. IEEE Trans Pattern Recognit Machine Intell, 1993,15 : 1186-1191.

共引文献1

同被引文献19

  • 1杨琨,曾立波,王殿成.数学形态学腐蚀膨胀运算的快速算法[J].计算机工程与应用,2005,41(34):54-56. 被引量：43
  • 2Datta R,Joshi D,Li J,et al.Image retrieval:Ideas,influences,and trends of the new age[J].ACM Computing Surveys,2008,40(2):1-60.
  • 3Zhang L,Wang L,Lin W,et al.Geometric optimum experimental design for collaborative image retrieval[J].Circuits and Systems for Video634Technology,IEEE Transactions on,2014,24(2):346-359.
  • 4Gonzalez-Diaz I,Baz-Hormigos C E,Diaz-de-Maria F.A generative model for concurrent image retrieval and ROI segmentation[J].IEEE Transactions on Multimedia,2014,16(1):169-183.
  • 5Ojala T,Pietikainen M,Harwood D.A comparative study of texture measures with classification based on feature distributions[J],Pattern Recognition,1996,29(2):51-59.
  • 6Guo Z,Zhang L,Zhang D.A completed modeling of local binary pattern operator for texture classification[J].Image Processing,IEEE Transactions on,2010,19(6):1657-1663.
  • 7Singha M,Hemachandran K,Paul A.Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram[J].IET Image Processing,2012,6(9):1221-1226.
  • 8Atto A M,Berthoumieu Y,Bolon P.2-D wavelet packet spectrum for texture analysis[J].IEEE Transactions on Image Processing,2013,22(6):2495-2500.
  • 9Do M N,Vetterli M.The contourlet transform:an efficient directional multiresolution image representation[J].IEEE Transactions on Image Processing,2005,14(12):2091-2106.
  • 10Lu Y,Do M N.A new contourlet transform with sharp frequency localization[C]//Proc of IEEE International Conference on Image Processing,Atlanta,USA,2006:8-11.

引证文献3

二级引证文献2

信阳师范学院学报（自然科学版）
2014年 第3期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部