综合颜色和形状特征聚类的图像检索

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3549-3553.

综合颜色和形状特征聚类的图像检索

张永库¹,²,李云峰²,孙劲光²

1.
2. 辽宁工程技术大学电子与信息工程学院, 辽宁葫芦岛 125105

收稿日期:2014-05-22 修回日期:2014-07-11 出版日期:2014-12-01 发布日期:2014-12-31
通讯作者: 李云峰
作者简介:张永库(l972-),男,辽宁阜新人,副教授, 硕士,主要研究方向:图形图像处理、多媒体、数据处理、数据挖掘；李云峰(1989-),男,山东青岛人,硕士研究生,主要研究方向:图像检索、图形图像处理；孙劲光(1962-),女,河北邯郸人, 教授, 博士, CCF会员, 主要研究方向:图形图像处理、人脸识别、数据挖掘。
基金资助:
国家科技支撑计划项目

Image retrieval based on clustering according to color and shape features

ZHANG Yongku¹,²,LI Yunfeng²,SUN Jingguang²

1.
2. College of Electronics and Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China

Received:2014-05-22 Revised:2014-07-11 Online:2014-12-01 Published:2014-12-31
Contact: LI Yunfeng

摘要/Abstract

摘要：

为了提高图像检索的速度和准确率,通过分析各种聚类算法在图像检索中的缺点,提出了一种新的划分聚类的图像检索方法。首先对HSV模型非均匀量化,利用改进的颜色聚合向量方法提取图像的颜色特征；然后基于改进的Hu不变矩提取图像的全局形状特征；最后,综合颜色和形状特征对图像基于贡献度聚类并建立特征索引库。利用上述方法在Corel图像库中进行图像检索。实验结果表明,与改进的K-means算法的图像检索算法相比,提出算法的查准率和查全率均有较大提高。

Abstract:

In order to improve the speed and accuracy of image retrieval, the drawbacks of image retrieval based on a variety of clustering algorithms were analyzed, then a new partition clustering method for image retrieval was presented in this paper. First, based on the asymmetrical quantization of the color in HSV model, color feature of image was extracted by color coherence vectors. Then, global shape feature of image was extracted based on improved Hu invariant moment. Finally,images were clustered based on contribution according to color and shape features, and image feature index library was established. The methods described above were used for image retrieval based Corel image library. The experimental results show that compared with image retrieval algorithms based on improved K-means algorithms, precision ratio and recall ratio of the proposed algorithm are improved greatly.

中图分类号:

TP391.4

张永库李云峰孙劲光. 综合颜色和形状特征聚类的图像检索[J]. 计算机应用, 2014, 34(12): 3549-3553.

ZHANG Yongku LI Yunfeng SUN Jingguang. Image retrieval based on clustering according to color and shape features[J]. Journal of Computer Applications, 2014, 34(12): 3549-3553.

参考文献

［1］DAHANE G M, VISHWAKARMA S. Content based image retrieval system［J］. International Journal of Engineering and Innovative Technology, 2012, 1(5): 92-96.
［2］ZHENG Q, YU F, GUO L. An image retrieval algorithm based on multiple color space［J］. Journal of Computer Applications, 2006, 26(11): 2707-2709.(郑秋梅,余芳,郭兰图. 基于多颜色空间的图像检索算法［J］.计算机应用,2006, 26(11): 2707-2709.)
［3］QU H. Texture image retrieval based on complementary features［J］. Journal of Computer Applications, 2012,32(4): 1101-1103,1107. (曲怀敬. 基于互补特征的纹理图像检索［J］. 计算机应用,2012,32(4): 1101-1103,1107.)
［4］ZHANG W, TANG J. Image retrieval method based on NMI invariable feature［J］. Journal of Computer Applications, 2003,23(9):55-57.(张问银,唐建国. 基于NMI不变特征的图像检索［J］. 计算机应用,2003,23(9):55-57.)
［5］SHI T, LI Y. Image retrieval based on spatial feature［J］. Journal of Computer Applications, 2008, 28(9): 2292-2296. (史婷婷,李岩. 基于空间特征的图像检索［J］. 计算机应用,2008,28(9):2292-2296.)
［6］SINGHAI N, SHANDILYA S K. A survey on: content based image retrieval systems［J］. International Journal of Computer Applications, 2010, 4(4): 22-26.
［7］JAIN M, SINGH S K. A survey on: content based image retrieval systems using clustering techniques for large data sets［J］. International Journal of Managing Information Technology, 2011, 3(4):23-39.
［8］ZHOU H Y, SADKA A H, SWASH M R, et al. Content-based image retrieval and clustering: a brief survey［J］. Recent Patents on Electrical Engineering, 2009, 2(3):187-199.
［9］SHI X, XUE A, LIU Y. Application study of improved k-means clustering algorithm in image retrieval［J］. Computer Engineering and Applications, 2011, 47(10):193-196,248.(史习云,薛安荣,刘艳红. 改进k-means聚类算法在图像检索中的应用研究［J］.计算机工程与应用,2011,47(10):193-196,248.)
［10］LYU M,LIU D, ZENG Z. An improved K-means clustering algorithm for image retrieval algorithm［J］.Computer Science, 2013,40(8):285-288.(吕明磊,刘冬梅,曾智勇. 一种改进的K-means聚类算法的图像检索算法［J］. 计算机科学,2013,40(8):285-288.)
［11］SAKTHIVEL K,ABINAYA R,NIVETHA I,et al. Region based image retrieval using k-means and hierarchical clustering algorithms ［J］. International Journal of Innovative Research in Science, Engineering and Technology, 2014, 3(1):1255-1260.
［12］LIN C-H, CHEN C-C, LEE H-L, et al. Fast K-means algorithm based on a level histogram for image retrieval［J］. Expert Systems with Applications, 2014, 41(7): 3276-3283.
［13］PASS G, ZABIH R. Histogram refinement for content-based image retrieval［J］. ACM Journal of Multimedia Systems, 1999, 7(3): 234-240.
［14］XIE H, LU Y, SUN S. An image retrieval method based on semi-supervised learning［J］.Application Research of Computers,2013,30(7):2210-2212.(谢辉, 陆月明, 孙松林. 基于半监督学习的一种图像检索方法［J］. 计算机应用研究, 2013, 30(7): 2210-2212.)
［15］ZHANG W, LI X. Research of image retrieval based on color and shape features［C］// Proceedings of the 2011 International Conference on Electric Information and Control Engineering. Piscataway: IEEE,2011: 4378-4381.
［16］SHEN G, JIANG L. An image retrieval algorithm based on color segment and shape moment invariants［C］// Proceedings of the 2nd International Symposium on Computational Intelligence and Design. Piscataway: IEEE, 2009:517-521.
［17］OSBORNE M J. An introduction to game theory［M］. Oxford: Oxford University Press, 2007: 41-43.
［18］GARG V K. Pragmatic data mining: novel paradigms for tackling key challenges ［D］. Bangalore: Indian Institute of Science, 2009.

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