To retain the position relationship of pixels when image analyzing and dimension reducing, we extend the one-dimensional compressive sensing theory to two-dimensional, and establish a two-dimensional compressive measurement model for sparse signal.We study an adaptive gradient descent recursion algorithm for two-dimensional signal, and propose an image hierarchical feature extraction and retrieval method.Firstly, it conducts grid discrete division on the RGB color space, and mapping to the image by hierarchical operator.It defines an extended GLCM based on color grid space, and extracts the hierarchical measurement feature, texture feature and hierarchical color statistical feature by the two-dimensional measurement model.The hierarchical measurement feature of image reflects the position relationship between the image color and pixel, and the extended GLCM reflects the texture feature.Secondly, it calculates the original signal difference and sparse value between images by the AGDR algorithm.Finally, it calculates the overall similarity metrics between images by combining the two hierarchical feature difference, the sparse value and the color statistical feature.The simulation results show that the image retrieval method which applying hierarchical two-dimensional compressive sensing measurement and AGDR algorithm has superior performance on retrieval time, recall and precision, it provides a new idea for the image retrieval.
周燕, 曾凡智. 基于二维压缩感知和分层特征的图像检索算法[J]. 电子学报, 2016, 44(2): 453-460.
ZHOU Yan, ZENG Fan-zhi. An Image Retrieval Algorithm Based on Two-Dimensional Compressive Sensing and Hierarchical Feature. Chinese Journal of Electronics, 2016, 44(2): 453-460.
[1] 杨育彬,陈世福,林珲.一种基于颜色连通的图像纹理检索新方法[J].电子学报,2005,33(1):57-62.
[2] Mohsen Zand,Shyamala Doraisamy,Alfian Abdul Halin.Texture classification and discrimination for region-based image retrieval[J].J.Vis.Comm.Image R,2015,26:305-316.
[3] Xiaoyu Wang,Ming Yang,Timothee Cour,et al.Contextual Weighting for Vocabulary Tree based Image Retrieval[C].ICCV2011,pp.209-216,in Barcelona,Spain,November,2011.
[4] Liang Zheng,Shengjin Wang,Ziqiong Liu,et al.Packing and padding:coupled multi-index for accurate image retrieval[C].CVPR2014,pp.1947-1954,in Columbus,Ohio,USA,June,2014.
[5] Yong Xu,Hui Ji.Viewpoint invariant texture description using fractal analysis[J].Int.J Comput Vis,2009,83:85-100.
[6] Yong Xu,Sibin Huang,Hui Ji.Scale-space texture description on SIFT-like textons[J].Computer vision and image understanding,2012,116:999-1013.
[7] Donoho D.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[8] Candes E,Wakin M.An introduction to compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):21-30.
[9] Mahdi Cheraghchi,Venkatesan Guruswami,Ameya Velingker.Restricted isometry of fourier matrices and list decodability of random linear codes[J].Proceedings of the ACM-SIAM Symposium on Discrete Algorithms(SODA),2013.
[10] 许志强.压缩感知[J].中国科学:数学,2012,42(9):865-877.
[11] 周燕,曾凡智,赵慧民等.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.
[12] Lu Gan.Block compressed sensing of natural images[C].//15 th Inter.Conf.on Digital Signal Proc.,2007.
[13] Duarte M F,Davenport M A,Takhaar D,et al.Single pixel image via compressed sampling[J].IEEE Signal Proc.,2008,25(2):83-91.
[14] B.Han.F.,Wu.D.Pl.Image representation by compressed sensing for visual sensor networks[J].J.Vis Comm.,2010,21(4):325-333.
[15] Gao Chen,Defang Li,Jiashu Zhang.Iterative gradient projection algorithm for two-dimensional compressed sensing sparse image reconstruction[J].Signal Processing,2014,104:15-26.
[16] 宗竹林,胡剑浩,朱立东,等.编队卫星合成孔径空时二维压缩感知成像[J].电波科学学报,2012,27(3):626-631.
[17] 程涛,朱国宾,刘玉安.基于二维压缩感知的定向遥测与变化检测[J].红外与毫米波学报,2013,32(5):456-461.
[18] 周燕,曾凡智,卢炎生,等.面向制造领域的三视图模型组件快速检索方法研究[J],中山大学学报(自然科学版),2014,53(04):62-68.
[19] 程涛,朱国宾,李小龙.压缩感知的等效二维稀疏变换[J].半导体光电,2014,35(6):1119-1122.
[20] A Eftekerhar,M Babaie-Zadeh,et al.Two-dimensional random projection[J],Signal Processing,2011,91(7):1589-1603.
[21] Nishant Shrivastava,Vipin Tyagi.An efficient technique for retrieval of color images in large databases[J].Computers and Electrical Engineering,2014,11(9):1-14.
[22] M E ElAlami,ElAlami ME.A novel image retrieval model based on the most relevant features[J].Knowl-Based Syst 2011,24:23-32.
[23] Lin Chuen-Horng et al.A smart content-based image retrieval system based on color and texture feature[J].Image Vis Compute,2009,27:658-65.
2016, Vol. 44 
