Remote sensing image retrieval method integrating feature similarity measurement and SVM

doi:10.7523/j.issn.1002-1175.2013.03.011

Abstract

Abstract:

The support vector machine (SVM)-based relevance feedback algorithm has been used in common image retrieval, but not widely applied to remote sensing images. Traditional algorithm only uses SVM classifiers, resulting in some wrong ranking sequences of retrieval results. An improved relevance feedback strategy is proposed, and it modifies the similarity measurement criterion using a weighted linear combination of feature similarity measurement and SVM classifier. Experimental results show that the proposed method improves the ranking sequence and accuracy of retrieval results.

Key words: remote sensing image retrieval, support vector machine, relevance feedback, similarity measurement

CLC Number:

TP751

ZHAO Li-Jun, TANG Jia-Kui, YU Xin-Ju, WANG Chun-Lei, ZHANG Cheng-Wen. Remote sensing image retrieval method integrating feature similarity measurement and SVM[J]. , 2013, 30(3): 347-352.

References

[1] Suman K P, Jawahar C V. Analysis of relevance feedback in content based image retrieval[C]//9th International Conference on Control, Automation, Robotics and Vision. Singapore, 2006:1-6.

[2] Cristianini N,Shawe Taylor J.An introduction to support vector machine[M].New York:Cambridge University Press,2000.

[3] Jiang N N, Qi M, Hao C Y. A SVM based relevance feedback algorithm for image retrieval[J]. Computer Simulation, 2009, 26(1):219-221(in Chinese). 姜楠楠, 齐敏, 郝重阳. 一种基于SVM的相关反馈图像检索算法[J]. 计算机仿真, 2009, 26(1):219-221.

[4] Zhou J X, Gao K, Li J T, et al. Efficient relevance feedback scheme based on SVM in image retrieval[J]. Journal of Computer-aided Design& Computer Graphics, 2007, 19(4):535-540(in Chinese). 周建新, 高科, 李锦涛,等. 图像检索中一种有效的SVM 相关反馈算法[J]. 计算机辅助设计与图形学学报, 2007, 19(4): 535-540.

[5] Seo K K. An application of one-class support vector machines in content-based image retrieval[J]. Expert Systems with Applications, 2007, 33(2): 491-498.

[6] Jiang W, Er G H, Dai Q H. Boost SVM active learning for content-based image retrieval[C]//Conference Record of the Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, United States, 2003: 1585-1589.

[7] Hoi S C H, Lyu M R, Jin R. Integrating user feedback log into relevance feedback by coupled SVM for content-based image retrieval[C]//International Workshop on Biomedical Data Engineering. Tokyo, Japan, 2005: 1177.

[8] Yang Y, Newsam S. Geographic image retrieval using local invariant features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2):818-832.

[9] Cheng Q M,Yang C J,Chen F X,et al.Application of M-band wavelet theory to texture analysis in content-based aerial image retrieval[C]//2004 IEEE International Geoscience and Remote Sensing Symposium.Anchorage,AK,2004:2163-2165.

[10] Liu L, Kuang G Y. Overview of image textural feature extraction methods[J]. Journal of Image and Graphics, 2009, 14(4):622-635(in Chinese). 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.

[11] Yue J, Li Z B, Liu L, et al. Content-based image retrieval using color and texture fused features[J]. Mathematical and Computer Modelling, 2011, 54(3/4):1121-1127.

[12] Balamurugan V, Anandha Kumar P. An integrated color and texture feature based framework for content based image retrieval using 2D Wavelet Transform[C]//International Conference on Computing, Communication and Networking. St Thomas, VI, 2008: 1-16.

[13] Haralick R M, Shanmugam K, Dinstein I. Texture features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, 3(6): 610-621.

[14] Du P J, Tang H, Fang T. Several issues of content-based remote sensing image retrieval[J]. Journal of China University of Mining & Technology, 2005,34(3):270-273(in Chinese). 杜培军,唐宏,方涛. 基于内容的遥感影像检索若干问题的研究[J]. 中国矿业大学学报,2005,34(3):270-273.

[15] Zha Y, Ni S X, Yang S. An effective approach to automatically extract urban land use from TM image[J]. Journal of Remote Sensing, 2003, 7(1):37-41(in Chinese). 查勇,倪绍祥,杨山.一种利用TM图象自动提取城镇用地信息的有效方法[J].遥感学报, 2003, 7(1):37-41.

[16] 周成虎,骆剑承,杨晓梅,等.遥感影像地学理解与分析[M].北京:科学出版社,1999.

[17] Vapnik V N. Statistical learning theory[M]. New York: Wiley, 1998.

[18] Vapnik V N. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1999.

[19] Zhang L, Lin F Z, Zhang B. Support vector machine learing for image retrieval[C]//2001 International Conference on Image Processing. Thessaloniki, 2001, 2:721-724.

[20] Wang X J, Yang L L. Application of SVM relevance feedback algorithms in image retrieval[C]//International Symposium on Information Science and Engineering. Shanghai, 2008: 210-213.

[21] Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification [R]. Taiwan: Department of Computer Science and Information Engineering, National Taiwan University, 2004.