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New Technology of Library and Information Service  2014, Vol. 30 Issue (5): 90-95    DOI: 10.11925/infotech.1003-3513.2014.05.12
RESEARCH ON APPLICATION Current Issue | Archive | Adv Search |
Implementation and Application of Image Retrieval Technology in “Classic Reading” Teaching System
Wu Kun1, Xie Xiaqing2, Bai Quanwei3, Wu Xu2, 3
1 Ministry of Culture Foundation, Changchun Finance College, Changchun 130028, China;
2 Beijing University of Posts and Telecommunications Library, Beijing 100876, China;
3 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT),Ministry of Education, Beijing 100876, China
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Abstract  

[Objective] This paper trends to expand retrieval approach in “Classic Reading” Teaching System and improve utilization of classical teaching resources. [Context] “Classic Reading” Teaching System is a credit-based innovation platform on teaching system, and adding image retrieval function can greatly extend the existing text-based retrieval and improve teaching effects. [Methods] This paper establishes the Semantic-Based Image Retrieval Model including extracting features, vector normalization and similarity measurement, realizing four modules including query-submit, image-retrieval, result-feedback and image management. [Results] The images in the platform are classified automatically and students can find the book with a related image, and the precise of image retrieval lays between 92% and 100%. [Conclusions] It can improve user experience as well as the teaching effects of “Classic Reading”.

Key wordsFeature-Semantical      Image retrieval      Feature vectors      Classic reading      Quality education     
Received: 14 December 2013      Published: 06 June 2014
:  G358TP391  

Cite this article:

Wu Kun, Xie Xiaqing, Bai Quanwei, Wu Xu. Implementation and Application of Image Retrieval Technology in “Classic Reading” Teaching System. New Technology of Library and Information Service, 2014, 30(5): 90-95.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2014.05.12     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2014/V30/I5/90

[1] 阿斯艳 哈米提, 阿不都热西提·哈米提. 基于文本的图像检索与基于内容的图像检索技术的比较研究[J]. 首都师范大学学报: 自然科学版, 2012, 33(4): 6-9. (Asiyan Hamiti, Abudurexiti Haimiti. Comparative Research of Image Retrieval based on Text and Content[J]. Journal of Capital Normal University: Natural Sciences Edition, 2012, 33(4): 6-9.)
[2] Stehling R O, Nascimento M A, Falcão A X. An Adaptive and Efficient Clustering-Based Approach for Content-Based Image Retrieval in Image Databases[C]. In: Proceedings of 2001 International Symposium on Database Engineering & Applications, Grenoble, France. IEEE, 2001: 356-365.
[3] Swets D L, Weng J J. Efficient Content-based Image Retrieval Using Automatic Feature Selection Computer Vision[C]. In: Proceedings of International Symposium on Computer Vision, Coral Gables, FL, USA. IEEE, 1995: 85-90.
[4] Heczko M, Hinneburg A, Keim D A, et al. Multiresolution Similarity Search in Image Databases [J].Multimedia Systems, 2004, 10(1): 28-40.
[5] Cho S B, Lee J Y. A Human-oriented Image Retrieval System Using Interactive Genetic Algorithm [J]. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2002, 32(3): 452-458.
[6] Xu X, Zhang L, Yu Z, et al. Image Retrieval Using Multi-granularity Color Features [C]. In: Proceedings of International Conference on Audio, Language and Image Processing (ICALIP'08), Shanghai, China. IEEE, 2008: 1584-1589.
[7] Verma B, Kulkarni S. A Fuzzy-neural Approach for Interpretation and Fusion of Color and Texture Features for CBIR Systems [J]. Applied Soft Computing, 2004, 5(1): 119-130.
[8] Gagaudakis G, Rosin P L. Incorporating Shape into Histograms for CBIR [J]. Pattern Recognition, 2002, 35(1): 81-91.
[9] Nascimento M A, Sridhar V, Li X. Effective and Efficient Region-based Image Retrieval [J]. Journal of Visual Languages & Computing, 2003, 14(2): 151-179.
[10] Alemu Y, Koh J, Ikram M, et al. Image Retrieval in Multimedia Databases: A Survey [C]. In: Proceedings of the 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing(IIH-MSP'09), Kyoto, Japan. IEEE, 2009: 681-689.
[11] Drupal.org[EB/OL].[2014-03-05]. https://drupal.org/.
[12] Furht B. Video and Image Processing in Multimedia Systems: Special Issue [J]. Real-Time Imaging, 1996, 2(1): 1-2.
[13] 薄华, 马缚龙, 焦李成. 图像纹理的灰度共生矩阵计算问题的分析[J].电子学报, 2006, 34(1): 155-158.(Bo Hua, Ma Fulong, Jiao Licheng. Research on Computation of GLCM of Image Texture[J]. Acta Electronica Sinica, 2006, 34(1): 155-158.)
[14] 黄勇, 王崇骏, 王亮, 等.基于形状不变矩的图像检索算法的研究[J].计算机应用研究, 2004, 21(7): 256-257, 260.(Huang Yong, Wang Chongjun, Wang Liang, et al. Research on Image Retrieval Algorithm Based on Moment Invariant[J]. Application Research of Computers, 2004, 21(7): 256-257, 260.)
[15] Ortega M, Rui Y, Chakrabarti K, et al. Supporting Similarity Queries in MARS[C]. In: Proceedings of the 5th ACM International Conference on Multimedia. New York: ACM, 1997: 403-413.
[16] Wikimedia. Euclidean Distance [EB/OL]. [2014-03-05]. http://en.wikipedia.org/wiki/Euclidean_metric.
[17] Altman N S. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression [J]. The American Statistician, 1992, 46(3): 175-185.
[18] The Apache Software Foundation.Welcome to ApacheTM Hadoop[EB/OL].[2014-03-05]. http://hadoop.apache.org/.
[19] Dean J, Ghemawat S.MapReduce: Simplified Data Processing on Large Clusters [J]. Communications of the ACM, 2008, 51(1): 107-113.

[1] Xie Xiaqing, Wu Xu. Application of Visualization Technology for “Classic Reading” Platform[J]. 现代图书情报技术, 2015, 31(11): 96-103.
[2] Fang Naiwei, Lv Xueqiang, Zhang Dan. Mechanical Design Image Retrieval with Combined Geometrical Features[J]. 现代图书情报技术, 2013, 29(1): 43-49.
[3] Cheng Xiufeng, Zhu Song, Xia Lixin. Design and Application of Distributed Image Retrieval Based on Color Model to Histogram Conversion[J]. 现代图书情报技术, 2011, 27(5): 42-48.
[4] Cao Mei,Zhu Xuefang. Research Progress on User Image Descriptions[J]. 现代图书情报技术, 2009, 25(12): 31-36.
[5] Zhao Ying,Liu Jiajia . Remote Sensing Image Retrieval Based on Bayes Classification[J]. 现代图书情报技术, 2006, 1(5): 36-39.
[6] Huang Kun,Lai Maosheng . A Study on Kansei Engineering and Its Application in Image Retrieval[J]. 现代图书情报技术, 2006, 1(5): 31-35.
[7] Yuan Fuyong,Wang Haixia,Yang Zhiqiu. Research for Texture Analysis in Content-Based Image Retrieval[J]. 现代图书情报技术, 2006, 22(1): 59-61.
[8] Zhou Ning,Yang Chuanzhi,Wu Jiaxin. A Method of Image Index and Retrieval Using XML[J]. 现代图书情报技术, 2005, 21(9): 32-35.
[9] Wu Jiaxin,Zhou Ning,Zhang Shaolong. The Database Approach for Image Indexing and Retrieval[J]. 现代图书情报技术, 2005, 21(7): 11-14.
[10] Huang Kun,Lai Maosheng. Research on the Techniques of Trademark Image Retrieval[J]. 现代图书情报技术, 2004, 20(4): 32-36.
[11] Meng Xiangzeng,Zhong Yixin. Images Retrieval Based on Semantic in WWW[J]. 现代图书情报技术, 2004, 20(3): 35-37.
[12] Xiong Huixiang. Development Direction of Content Based Image Retrieval[J]. 现代图书情报技术, 2004, 20(12): 32-35.
[13] Kong Tao,Lai Maosheng. Meaning Association-Based Chinese Image Search Engine[J]. 现代图书情报技术, 2002, 18(3): 63-65.
[14] Wang Xin. WWW-Based Image Retrieval Techniques[J]. 现代图书情报技术, 2002, 18(3): 70-73.
[15] Wen Yanping. Research on Content-based Image Retrieval System[J]. 现代图书情报技术, 2001, 17(1): 45-47.
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