吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (05): 1408-1414.doi: 10.7964/jdxbgxb201305041

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Image retrieval based on IPDSH and region division

ZHANG Xu, GUO Bao-long, MENG Fan-jie, SUN Wei   

  1. Institute of Intelligent Control & Image Engineering, Xidian University, Xi'an 710071, China
  • Received:2012-10-29 Online:2013-09-01 Published:2013-09-01

Abstract:

The accuracy of the current interest points detection algorithms are usually influenced by the unstable interest points in the non-interest regions. To overcome this disadvantage, an image retrieval algorithm combining interest point detection and region division is proposed. First, the interest points detection algorithm, combining the feature of Scale-Invariant Feature Transform (SIFT) and Harris (IPDSH), detects the stable interest points. Second, using the spatial positions of the stable interest points, the algorithm divides the image into annular region and convex hull, and calculates the color histogram of pixels in the convex hull and the pseudo-Zernike in the local field of stable interest points in the annular region. Finally, the algorithm retrieves the image with the weighted feature vector. Experimental results show that the proposed method possesses high retrieval speed, simple realization, robustness in rotation and translation, and can reduce the impact of unstable interest points. The image retrieval precision can by improve by 7.0%-15.1%.

Key words: information processing technology, image retrieval, interest points, region division, feature extraction

CLC Number: 

  • TN919.8

[1] Schmid C, Mohr R. Local gray value invariants for image retrieval[J]. IEEE Transaction on PAMI, 1997, 19(5):530-535.

[2] Mikoljczyk K, Schmid C. Indexing based on scale-Invariant feature[C]//Proc of International Conference on Computer Vision, Vancouver, 2001.

[3] Shao H, Ferrari V, Svoboda T, et al. Fast indexing for image retrieval based on local appearance with re-ranking[C]//Proceedings of IEEE International Conference on Image Processing, Barcelona,Spain,2003.

[4] 汪华章,何小海,宰文姣. 基于局部和全局特征融合的图像检索[J]. 光学精密工程,2008,16 (6): 1098-1104. Wang Hua-zhang, He Xiao-hai, Zai Wen-jiao. Image retrieval based on combining local and global features[J]. Optics and Precision Engineering, 2008, 16(6): 1098-1104.

[5] 符祥,曾接贤. 基于兴趣点匹配和空间分布的图像检索方法[J]. 中国激光,2010,37(3):774-778. Fu Xiang, Zeng Jie-xian. A novel image retrieval method based on interest points matching and distribution[J]. Chinese Journal of Lasers, 2010, 37(3):774-778.

[6] 陈绵书,杨树媛,赵志杰,等. 多点多样性密度算法及其在图像检索中的应用[J]. 吉林大学学报:工学版,2011,41(5): 1456-1460. Chen Mian-shu, Yang Shu-yuan, Zhao Zhi-jie,et al. Multi-points diverse density learning algorithm and its application in image retrieval[J]. Journal of Jilin University(Engineering and Technology Edition), 2011, 41(5): 1456-1460.

[7] Zheng X, Zhou M, Wang X C. Interest point based medical image retrieval[C]//Lecture Notes in Computer Science.Beijing:Springer Verlag, 2008.

[8] 陈慧婷,覃团发,唐振华,等. 综合纹理统计模型与全局主颜色的图像检索方法[J]. 北京邮电大学学报,2011, 34:100-103, 118. Chen Hui-ting, Qin Tuan-fa, Tang Zhen-hua, et al. A method of image retrievals based on texture probability statistics and global dominant color[J]. Journal of Beijing University of Posts and Telecommunications, 2011, 34:100-103, 118.

[9] 曾智勇,张学军,崔江涛,等. 基于显著兴趣点颜色及空间分布的图像检索新方法[J]. 光子学报,2006,35(2):308-311. Zeng Zhi-yong, Zhang Xue-jun, Cui Jinag-tao, et al. A novel image retrieval algorithm based on color and distribution of prominent interest points[J]. Acta Photonica Sinca, 2006,35(2): 308-311.

[10] 全燕鸣,黎淑梅. 大型工件测量系统中的快速图像拼接方法[J]. 华南理工大学学报:自然科学版,2011,39 (8): 60-65. Quan Yan-ming, Li Shu-mei. Fast image mosaic method for large-scale workpiece measurement system[J]. Journal of South China University of Technology (Natural Science), 2011, 39 (8): 60-65.

[11] Preparata F P, Shamos M I. Computational Geometry: an Introduction[M]. New York: Springer-Verlag, 1985.

[12] 孟繁杰,郭宝龙. 一种基于兴趣点颜色及空间分布的图像检索方法[J]. 西安电子科技大学学报:自然科学版,2005,32(2):256-259. Meng Fan-jie, Guo Bao-long. A novel image retrieval algorithm based on the color and distribution of interest points[J]. Journal of Xidian University(Natural Science), 2005, 32(2):256-259.

[13] 王向阳,陈景伟,于永健. 一种基于彩色边缘综合特征的图像检索方法[J]. 华南理工大学学报,2010,23(2):216-221. Wang Xiang-yang, Chen Jing-wei, Yu Yong-jian. Edge-based color image retrieval by using multiple features[J]. Pattern Recognition and Artificial Intelligence,2010, 23(2):216-221.

[14] Wu J H, Wei Z R, Li C Y. Color and texture feature for content based image retrieval[J]. International Journal of Digital Content Technology and Its Applications, 2010, 4(3):43-49.

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