一种基于精细化稀疏自适应匹配追踪算法的图像检索方法研究

doi:10.3969/j.issn.0372-2112.2014.12.018

电子学报 ›› 2014, Vol. 42 ›› Issue (12): 2457-2466.DOI: 10.3969/j.issn.0372-2112.2014.12.018

一种基于精细化稀疏自适应匹配追踪算法的图像检索方法研究

周燕¹, 曾凡智¹, 赵慧民², 卢炎生³, 周月霞¹

1. 佛山科学技术学院计算机系, 广东佛山 528000;
2. 广东技术师范学院电子与信息学院, 广东广州 510665;
3. 华中科技大学计算机学院, 湖北武汉 430074

收稿日期:2014-04-18 修回日期:2014-08-10 出版日期:2014-12-25 发布日期:2014-12-25
作者简介:周燕 女,1979年12月生于江西抚州.2004年毕业于东华理工大学信息工程学院,硕士学位.现为佛山科学技术学院计算机系副教授.主要研究方向:图像处理. E-mail:zhouyan791266@163.com;曾凡智 男,1965年1月生于湖北洪湖.1984年、1987年和2009年分别在厦门大学、中国科学院计算中心和华中科技大学获学士、硕士和博士学位.现为佛山科学技术学院计算机系教授.主要研究方向:图像处理、数据挖掘. E-mail: coolhead@126.com;赵慧民 男,1966年3月生于陕西,1997年、2001年分别在西北工业大学和中山大学攻读硕士、博士学位.现为广东技术师范学院教授,广东省图像图形学会常任理事,《电子学报》、《中山大学学报》等期刊审稿专家.主要研究方向是压缩感知理论和信息安全技术. E-mail:zhaohumin@tom.com;卢炎生 男,1949 年生,华中科技大学教授,博士生导师,主要研究方向为数据库与软件工程.周月霞女,1978年1月生于湖北监利.2003年毕业于长江大学电子信息工程学院,硕士学位.现为佛山科学技术学院电子信息工程系讲师.主要研究方向:图像处理,信息采集. E-mail:zyx19@tom.com
基金资助:
国家自然科学基金(No.61272381);广东省自然科学基金(No.1052800001000016,No.10452800001004185,No.S2012010008639);广东省教育厅高校优秀青年创新人才培育(No.2012LYM_0132);佛山市科技发展专项基金(No.2011AA100051,No.20121011010070);佛山科学技术学院2013年优秀青年人才培育

An Image Retrieval Method Based on Meticulous Sparsity Adaptive Matching Pursuit Algorithm

ZHOU Yan¹, ZENG Fan-zhi¹, ZHAO Hui-min², LU Yan-sheng³, ZHOU Yue-xia¹

1. Department of Computer Science, Foshan University, Foshan, Guangdong 528000, China;
2. Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China;
3. Computer Institute, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China

Received:2014-04-18 Revised:2014-08-10 Online:2014-12-25 Published:2014-12-25

摘要/Abstract

摘要：

基于压缩感知理论,研究了一种精细化稀疏自适应匹配追踪(Meticulous Sparsity Adaptive Matching Pursuit,MSAMP)算法,在此基础上提出了一种新的数字图像检索方法.首先对图像的RGB颜色、灰度共生矩阵按照列优先次序形成颜色及纹理的原始信号,然后对这两类信号采用分块压缩感知测量方法对图像进行分块测量,得到代表颜色特征和纹理特征的分块测量向量.其次利用MSAMP算法进行分块重构,计算出分块原始信号差量及其稀疏值.最后在图像检索时,通过计算图像的整体相似度,重点对差量的稀疏性进行估计,不需要精确恢复原始信号,从而减少迭代次数,加快检索速度.仿真实验表明,应用MSAMP算法的图像检索方法在检索速度和查准率等指标上具有较高的性能.

关键词: 压缩感知, 图像检索, 纹理特征, 颜色特征, 自适应匹配追踪

Abstract:

Based on compressed sensing theory,we research a meticulous sparsity adaptive matching pursuit algorithm,and propose a new method for digital image retrieval on this basis.Firstly,the original signal of color and vein are formed from RGB color and gray level co-occurrence matrix by order of column prior.Then,these two signals are measured by the blocked compressive sensing method,and measurement vectors are obtained which representing the color and texture features.Secondly,we reconstruct the image by blocks using the MSAMP(Meticulous Sparsity Adaptive Matching Pursuit) algorithm,and calculate the difference and sparse value between the original blocked signals.Finally,we calculate the overall image similarity,and focus on estimating the sparseness of measurement difference.Because it does no need to recover the original signal precisely,so it can reduce the number of iteration and accelerate the retrieval speed.Simulation results show that the retrieval speed and retrieval precision about this image retrieval algorithm based on compressive sensing signal have higher performance.

Key words: compressive sensing, image retrieval, veins feature, color feature, adaptive matching pursuit

中图分类号:

TP391

周燕, 曾凡智, 赵慧民, 卢炎生, 周月霞. 一种基于精细化稀疏自适应匹配追踪算法的图像检索方法研究[J]. 电子学报, 2014, 42(12): 2457-2466.

ZHOU Yan, ZENG Fan-zhi, ZHAO Hui-min, LU Yan-sheng, ZHOU Yue-xia. An Image Retrieval Method Based on Meticulous Sparsity Adaptive Matching Pursuit Algorithm[J]. Acta Electronica Sinica, 2014, 42(12): 2457-2466.

参考文献

[1] 何云峰,周玲,于俊清,等.基于局部特征聚合的图像检索方法[J].计算机学报,2011,34(11):2224-2233. He Yunfeng,Zhou Ling,Yu Junqin,et al.Image retrieval based on locally features aggregating[J].Chinese Journal of Computers,2011,34(11):2224-2233.(in Chinese)
[2] 庄凌,庄越挺,吴江琴,等.一种基于稀疏典型性相关分析的图像检索方法[J].软件学报,2012,23(5):1295-1304. Zhuang Ling,Zhuang Yueting,Wu Jiangnqin,et al.Image retrieval approach based on sparse canonical correlation analysis[J].Journal of Software,2012,23(5):1295-1304.(in Chinese)
[3] 冯松鹤,郎丛妍,须德.一种融合图学习与区域显著性分析的图像检索算法[J].电子学报,2011,39(10):2288-2294. Feng Songhe,Lang Congyan,Xu De.Combining graph learning and region saliency analysis for content based image Rretrieval[J].Acta Electronica Sinica,2011,39(10):2288-2294.(in Chinese)
[4] 李清勇,胡宏,施智平,等.基于纹理语义特征的图像检索研究[J].计算机学报,2006,29(1):116-123. Li Qingyong,Hu Hong,Shi Zhiping,et al.Research on texture based semantic image retrieval[J].Chinese Journal of Computers,2006,29(1):116-123.(in Chinese)
[5] 刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4):622-635. Liu Li,Kuang Gangyao.Overview of image textural feature extraction methods[J].Journal of Image and Graphics,2009,14(4):622-635.(in Chinese)
[6] 贺广南,杨育彬,阮佳彬,等.基于视觉一致性的图像检索[J].中国图象图形学报,2011,16(4):503-509. He Guangnan,Yang Yubin,Ruan Jiabin,et al.Image retrieval based on visual consistency[J].Journal of Image and Graphics,2011,16(4):503-509.(in Chinese)
[7] M E ElAlami.A novel image retrieval model based on the most relevant features[J].Knowledge-Based Systems,2011,24:23-32.
[8] 常哲,侯榆青,李明俐,等.综合颜色和纹理特征的图像检索[J].小型微型计算机系统,2011,32(1):161-164. Chang Zhe,Hou Yuqing,Li Mingli,et al.Image retrieval based on combined color with texture feature[J].Journal of Chinese Computer Systems,2011,32(1):161-164.(in Chinese)
[9] 孙君顶,郭启强,周雪梅.基于颜色和纹理特征的彩色图像检索[J].计算机工程与应用,2010,46(29):176-178. Sun Junding,Guo Qiqiang,Zhou Xuemei.Image retrieval based on color and texture features[J].Computer Engineering and Applications,2010,46(29):176-178.(in Chinese) [SD34]
[10] Donoho D.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[11] Candes E,Wakin M.An introduction to compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):21-30.
[12] Romberg J.Imaging via compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):14-20.
[13] 焦李成,杨淑媛,刘芳,等.压缩感知回顾与展望[J].电子学报,2011,39(7):1651-1662. Jiao Licheng,Yang Shuyuan,Liu Fang,et al.Development and prospect of compressive sensing[J].Acta Electronica Sinica,2011,39(7):1651-1662.(in Chinese)
[14] 练秋生,周婷.结合字典稀疏表示和非局部相似性的自适应压缩成像算法[J].电子学报,2012,40(7):1416-1422. Lian Qiusheng,Zhou Ting.Adaptive compressed imaging algorithm combined the sparse representation in the dictionaries with non-local similarity[J].Acta Electronica Sinica,2012,40(7):1416-1422.(in Chinese)
[15] Joel Goodman,Keith Forysthe,Benjamin Miller.Efficient reconstruction of block-sparse signals[J].IEEE Statistical Signal Processing Workshop,2011:629-632.
[16] Taner Ince,Arif Nacaroglu,Nurdal Watsuji.Nonconvex compressed sensing with partially known signal support[J].Signal Processing,2013,93(1):338-344.
[17] Haixiao Liu,Bin Song,Hao Qin,et al.An adaptive-ADMM algorithm with support and signal value detection for compressed sensing[J].IEEE Trans on Signal Processing Letters,2013,20(4):315-318.
[18] T Wimalajeewa,H Chen,P K Varshney.Performance limits of compressive sensing-based signal classification[J].IEEE Trans.on Signal Processing,2012,60(6):2758-2770.
[19] 周燕,曾凡智,卢炎生,等.基于压缩感知的图像检索方法研究[J].中山大学学报(自然科学版),2014,53(1):57-62. Zhou Yan,Zeng Fanzhi,Lu Yansheng,et al.An image retrieval method based on compressive sensing[J].Acta Scientiarum Naturalium Universitatis SunYatsen,2014,53(1):57-62.(in Chinese)
[20] Candes E J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus Mathematique,2008,346(9-10):589-592.
[21] Mahdi Cheraghchi,Venkatesan Guruswami,Ameya Velingker.Restricted isometry of Fourier matrices and list decodability of random linear codes[A].Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA),2013.[C].New Orleans:SIAM,2013.
[22] 许志强.压缩感知[J].中国科学:数学,2012,42(9):865-877. Xu Zhiqiang.Compressed sensing:a survey[J].Sci Sin Math,2012,42(9):865-877.(in Chinese)
[23] Joel Tropp,Anna Gilbert.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Trans on Information Theory,2007,53(12):4655-4666.
[24] J Wang,S Kwon,B Shim.Generalized orthogonal matching pursuit[J].IEEE Trans Signal Process,2012,60(12):6202-6216.
[25] Entao Liu,Vladimir N.Temlyakov.The orthogonal super greedy algorithm and applications in compressed sensing[J].IEEE Trans on Information Theory,2012,58(4):2040-2047.
[26] Thong T D,Gan L.Sparsity adaptive matching pursuit algorithm for practical compressed sensing[A].Proc of the Fortieth-Second Asilomar Conference on Signals System and Computers[C].Pacific Grove,CA:IEEE,2013.581-587.
[27] 甘伟,许录平,罗楠,等.一种自适应压缩感知重构算法[J].系统工程与电子技术,2011,33(9):1948-1953. Gan Wei,Xu Luping,Luo Nan,et al.Adaptive recovery algorithm for compressive sensing[J].Systems Engineer ing and Electronics,2011,33(9):1948-1953.(in Chinese)
[28] Jae Young Park,Han Lun Yap,Christopher J,et al.Concentration of measure for block diagonal matrices with applications to compressive sensing[J].Signal Processing,IEEE Transactions on Preprint,2011,59(12):1-30.

一种基于精细化稀疏自适应匹配追踪算法的图像检索方法研究

An Image Retrieval Method Based on Meticulous Sparsity Adaptive Matching Pursuit Algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	罗忠涛, 严美慧, 卢琨, 夏杭. 超视距雷达中射频干扰仿真与距离-多普勒图检测方法[J]. 电子学报, 2022, 50(5): 1174-1180.
[2]	吕明久, 马建朝, 韦旭, 陈文峰, 杨军, 马晓岩. 基于矩阵化原子范数的高频雷达距离-多普勒二维估计方法[J]. 电子学报, 2022, 50(5): 1150-1158.
[3]	苏林林, 陈亮, 陈菲菲, 周鑫, 焦振航, 刘钊良. 面向B5G/6G的GFDM信号高精度测距与定位研究[J]. 电子学报, 2022, 50(4): 849-859.
[4]	左婷, 王法松, 张建康, 李睿. 室内可见光通信系统中基于压缩感知的空移键控信号检测方法[J]. 电子学报, 2022, 50(1): 36-44.
[5]	冯西安, 寇思玮, 谭伟杰, 毕杨. 水声信号处理中的稀疏表示理论及应用[J]. 电子学报, 2021, 49(9): 1840-1851.
[6]	罗忠涛, 夏杭, 卢琨, 何子述. 超视距雷达中距离-多普勒图的瞬态干扰自动识别方法[J]. 电子学报, 2021, 49(7): 1279-1285.
[7]	裴翰奇, 杨春玲, 魏志超, 曹燕. 基于SPL迭代思想的图像压缩感知重构神经网络[J]. 电子学报, 2021, 49(6): 1195-1203.
[8]	禤韵怡, 杨春玲. 基于帧间组稀疏的两阶段递归增强视频压缩感知重构网络[J]. 电子学报, 2021, 49(3): 435-442.
[9]	李峰, 詹邦成, 辛蕾, 刘洋, 刘志佳, 肖化超. 基于新型联合感知矩阵的压缩学习目标识别技术[J]. 电子学报, 2021, 49(11): 2108-2116.
[10]	郑恩明, 陈新华, 周权斌, 李嶷, 杨鹤, 孟浩. 一种复域压缩感知目标方位估计方法[J]. 电子学报, 2021, 49(11): 2117-2123.
[11]	金泽芬芬, 侯志强, 余旺盛, 王鑫, 寇人可. 基于量子遗传算法的视觉目标跟踪[J]. 电子学报, 2020, 48(8): 1493-1501.
[12]	孙泽宇, 李传锋, 阎奔. 传感网中基于压缩感知的丢包匹配数据收集算法[J]. 电子学报, 2020, 48(4): 723-733.
[13]	陈兵兵, 曹欣远, 陈明生, 齐琦, 况晓静, 吴先良, 陈辉. 基于欠定方程的频谱峰值搜索算法及其在连续毫米波雷达系统中的应用[J]. 电子学报, 2020, 48(2): 369-374.
[14]	郑学炜, 杨春玲, 禤韵怡. CVS中基于视频运动特征的多假设-双稀疏重构算法[J]. 电子学报, 2020, 48(2): 249-257.
[15]	马彬, 王宏明, 谢显中. 基于二项分布改进的宽带压缩频谱检测方案[J]. 电子学报, 2020, 48(2): 243-248.