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New Technology of Library and Information Service  2011, Vol. 27 Issue (5): 42-48    DOI: 10.11925/infotech.1003-3513.2011.05.07
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Design and Application of Distributed Image Retrieval Based on Color Model to Histogram Conversion
Cheng Xiufeng1, Zhu Song2, Xia Lixin1
1. Department of Information Management, Huazhong Normal University, Wuhan 430079,China;
2. Center of Computing & Experimenting,South-Central University for Nationalities, Wuhan 430074,China
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Abstract  The retrieval efficiency and the retrieval capability are considered as two major problems existing in CBIR systems. In order to improve the retrieval efficiency,the authors present a new CBIR application based on large databases in distributed environment using DHCIR (Distributed Image Retrieval method based on Color Model to Histogram Conversion) algorithm by reviewing past researches in CBIR and distributed computing. A new image retrieval system using this method is also developed. The test results show this application can provide stable, fast and efficient image retrieval functions to improve the retrieval speed and accuracy.
Key wordsHistogram      Color space      Content-based      Image retrieval      Distribution     
Received: 21 April 2011      Published: 11 July 2011



Cite this article:

Cheng Xiufeng, Zhu Song, Xia Lixin. Design and Application of Distributed Image Retrieval Based on Color Model to Histogram Conversion. New Technology of Library and Information Service, 2011, 27(5): 42-48.

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[1] Eakins J, Graham M. Content-based Image Retrieval . JISC Technology Applications Programme Report, 1999.

[2] Salton G, Buckley C. Term Weighting Approaches in Automatic Text Retrieval[J]. Information Processing and Management,1988, 24(5): 513-523.

[3] Arkin E M, Chew L P, Huttenlocher D P, et al. An Efficiently Computable Metric for Comparing Polygonal Shapes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence Archive,1991,13(3):209-216.

[4] Huang T S, Mehratra S, Ramchandran K. Multimedia Analysis and Retrieval System (MARS) Project [J].Journal of VLSI Signal Processing,1998,20(1-2):137-150.

[5] Pentland A, Picard R W,Sclaroff S. Photobook: Content-based Manipulation of Image Databases[J]. International Journal of Computer Vision, 1996, 18(3):233-254.

[6] Wilensky R .The UC Berkeley Digital Library Project: Re-thinking Scholarly Information Dissemination and Use . In: Proceedings of the 3rd European Conference on Research and Advanced Technology for Digital Libraries.1999:853-854.

[7] Smith J R, Chang S F. VisualSeek: A Fully Automated Content-based Image Query System . In: Proceedings of the 4th ACM International Conference on Multimedia.1996:87-98.

[8] Yu C C, Jou F D, Lee C C,et al. Efficient Multi-resolution Histogram Matching for Fast Image/Video Retrieval[J]. Pattern Recognition Letters, 2008, 29(13): 1858-1867.

[9] Pan X, You Q, Liu Z, et al. 3D Shape Retrieval by Poisson Histogram[J]. Pattern Recognition Letters, 2011, 32(3): 787-794.

[10] Swain M J, Ballard D H. Color Indexing[J]. International Journal of Computer Vision, 1991,7(1):11-32.

[11] Levine B G, Stone J E, Kohlmeyer A. Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units—Radial Distribution Function Histogramming[J]. Journal of Computational Physics, 2011, 230(9):3556-3569.

[12] Quellec G, Lamard M, Cazuguel G, et al. Wavelet Optimization for Content-based Image Retrieval in Medical Databases Original Research Article[J].Medical Image Analysis, 2010, 14 (2): 227-241.

[13] Phan R, Androutsos D. Content-based Retrieval of Logo and Trademarks in Unconstrained Color Image Databases Using Color Edge Gradient Co-occurrence Histograms[J]. Computer Vision and Image Understanding, 2010,114(1): 66-84.

[14] Han J, Ma K K. Fuzzy Color Histogram and Its Use in Color Image Retrieval[J].IEEE Transactions on Image Processing, 2002,11 (8):944-952.

[15] Park J, An Y, Kang G, et al. Defining a New Feature Set for Content-Based Image Analysis Using Histogram Refinement[J]. International Journal of Imaging Systems and Technology, 2008,18 (2-3): 86-93.

[16] Lin S D, Chen K, Yang X. Image Indexing by Color Plane Moment[J]. International Journal of Imaging Systems and Technology, 2002,12(4):139-148.

[17] Mejdoub M, Fonteles L, BenAmar C, et al. Embedded Lattices Tree: An Efficient Indexing Scheme for Content Based Retrieval on Image Databases[J].Journal of Visual Communication and Image Representation, 2009,20(2): 145-156.

[18] Liu G H, Zhang L, Hou Y K,et al. Image Retrieval Based on Multi-text on Histogram[J]. Pattern Recognition, 2010,43 (7): 2380-2389.

[19] Brunelli R, Mich O. Histograms Analysis for Image Retrieval[J].Pattern Recognition, 2001,34(8):1625-1637.

[20] Efford N. Digital Image Processing: A Practical Introduction Using Java[M]. New Jersey: Addison Wesley, 2000.

[21] Cowlishaw M F. Fundamental Requirements for Picture Presentation[J]. Proceedings of the Society for Information Display,1985, 26(2): 101-107.

[22] Strelkov V V. A New Similarity Measure for Histogram Comparison and Its Application[J]. Pattern Recognition Letters, 2008,29(13):1768-1774.

[23] 何楚,王思贤,廖孟扬.基于内容的图像搜索的算法模型与分布式方案[J] . 小型微型计算机系统, 2002,23(5): 544-547.

[24] Stallings W. Computer Organization and Architecture: Designing for Performance[M]. Columbus, Ohio: Prentice Hall, 2006.
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