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

融合复小波特征和局部二值模式的纹理聚类

Texture clustering combining complex wavelet features and local binary pattern
在线阅读 下载PDF
导出
摘要 为了解决图像检索中聚类问题,对图像作双树旋转复小波变换,再对变换后的系数通过广义高斯模型建模后,计算Kullback-Leibler距离;对图像采用局部二值模式,计算图像之间的对数似然距离.将这2种距离采用事先计算得到的加权因子进行融合得到新的距离.在此距离的基础上构建邻接矩阵,对邻接矩阵采用谱聚类的方法进行聚类运算.实验证明,由于双树旋转复小波变换和局部二值模式之间存在互补性,在聚类过程中将2种特征距离结合起来,能够有效地提高聚类的正确性. A dual tree rotated complex wavelet （DT-RCW） transform was applied on images to solve the clustering problem in image retrieval. Kullback-Leibler distances were computed with the generalized Gaussian density model of each high-frequency band. A local binary pattern was employed on each image and the log-likelihood distances were computed. The two distances were combined with the pre-computed weight to produce new distance,on which an adjacent matrix was built. Then spectral clustering was performed with the generated adjacent matrix. Because the local binary pattern can be considered as the complementary feature of rotated complex wavelet,the experimental results show that combining the rotated complex wavelet and the local binary pattern can effectively improve the clustering performance.
作者 幸锐 徐舒畅 张三元 竺乐庆
机构地区 浙江大学计算机科学与技术学院 杭州师范大学信息科学与工程学院
出处 《浙江大学学报（工学版）》 EI CAS CSCD 北大核心 2010年第5期910-914,共5页 Journal of Zhejiang University：Engineering Science
基金 国家"863"高技术研究发展计划资助项目(2007AA01Z311 2007AA04ZA5) 国家"973"重点基础研究发展规划资助项目(2009CB320800) 国家自然科学基金资助项目(60473106) 浙江省科技计划资助项目(2009C31034)
关键词 双树旋转复小波 纹理 Kullback-Leibler距离 局部二值模式 dual tree rotated complex wavelet （DT-RCW） texture Kullback-Leibler distance local binary pattern
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献13

  • 1SMEULDERS A W M, WORRING M, SANTINI S, et al. Content-based image retrieval at the end of the early years [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1349- 1380.
  • 2ODOBEZ J M, GATICA-PEREZ D, GUILLEMOT M. Spectral structuring of home videos [C]// International Conference on Image and Video Retrieval (CIVR'03). Illinois: Springer, 2003: 85- 90.
  • 3CHEN Y, WANG J Z, KROVETZ R. Content-based image retrieval by clustering [C]// Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval. New York: ACM, 2003: 193- 200.
  • 4KINGSBURY N. Complex wavelets for shift invariant analysis and filtering of signals [J]. Applied and Computational Harmonic Analysis, 2001, 10(3) :234 - 253.
  • 5KOKARE M, BISWAS P K, CHATTERJI B N. Texture image retrieval using new rotated complex wavelet filters [J]. IEEE Transaction on System, Man and Cybernetics-Part B, 2005, 35(6) : 1168 - 1178.
  • 6WOUWER G V, SCHEUNDERS P, DYCK D V. Statistical texture characterization from discrete wavelet representation [J]. IEEE Transaction on Image Processing, 1999, 8(4): 592- 598.
  • 7MALLAT S. A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.
  • 8DO M N, VETTERLI M. Wavelet-based texture retrieval using generalized gaussian density and KullbackLeibler distance[J]. IEEE Transaction on Image Processing, 2002, 11(2) : 146 - 158.
  • 9练秋生,李芹,孔令富.融合圆对称轮廓波统计特征和LBP的纹理图像检索[J].计算机学报,2007,30(12):2198-2204. 被引量：15
  • 10SHI J, MALIK J. Normalized cuts and image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888- 905.

二级参考文献15

  • 1练秋生,孔令富.圆对称轮廓波变换的构造[J].计算机学报,2006,29(4):652-657. 被引量：12
  • 2Viscito E, Allebach J P. The analysis and design of multidimensional fir perfect reconstruction filter banks for arbitrary sampling lattices. IEEE Transactions on Circuits and Systems, 1991, 38(1): 29-41
  • 3Mallat S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
  • 4Do M N, Vetterli M. Wavelet-based texture retrieval using generalized gaussian density and Kullback-Leibler distance. IEEE Transactions on Image Processing, 2002, 11(2): 146- 158
  • 5Po D D Y, Do M N. Directional muhiscale modeling of images using the contourlet transform. IEEE Transactions on Image Processing, 2006, 15(6): 1610-1620
  • 6Smeulders A W M, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1349-1380
  • 7Manjunath B S, Ohm J R, Vasudevan V V, Yamada A. Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6): 703-715
  • 8Borber M. MPEG-7 visual shape descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6): 716-719
  • 9Haralick R M, Shanmugam K, Dinstein I. Texture feature for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 1973, 3(6): 610-621
  • 10Tamura H, Mori S, Yamawaki T. Texture features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 1978, 8(6): 460-473

共引文献14

浙江大学学报（工学版）
2010年 第5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