Abstract
This paper mainly introduced the multimedia information technology in the image retrieval application, the basic principle which coupled between the multimedia information technology and the image retrieval and the application of the sift algorithm. Through a number of feature vectors and characteristics of the target, a new image retrieval system was designed. According to the preferences of the user, the system can generate a user query log and automatically add more search information; it’s a great convenience to the user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lee D-H, Seo D-Y, Kim N-H, Lee J-Y (1998) Discovery and application of user access patterns in the World Wide Web. In: Proceedings of the 4th world congress on expert systems, IEEE CS, vol 16, pp 321–327
Niblack W, Barber R, Equitz W, Flickner M, Glasman E, Petkovic D, Yanker P, Faloutsos C (1993) The QBIC project: querying images by content using color, texture, and shape. In: Proceedings of the SPIE storage and retrieval for image and video database, San Jose, vol 2, pp 173–187
Zhuang Yueting, Pan Yunhe, Wu Fei (2002) The online multimedia information analysis and retrieval. Tsinghua University press, Bei**g
Yuan Fang, Liu Ming (2001) The content based image retrieval technology in the digital library. J Info 14:45–49
Wang Huifeng, Sun Zhengxing (2001) The semantic processing method in the content based image retrieval. China J Image Graph 10:7–9
Li J, Wang JZ, Wiederhold G (2000) Integrated region matching for image retrieval. In: Proceedings of the 2000 ACM multimedia conference, Los Angeles, vol 9, pp12–15
Zhu **ngquan, Zhang Hongjiang, Liu Wenyin (2002) A image relevance feedback retrieval system based on the combination of the semantics and the visual features. Comput J 07:123–128
Chen **g (2005) Using clustering algorithm to improve the accuracy of image retrieval. Comput Aided Eng 1:17–20
**a Dingyuan (2004) The content based image retrieval technology research and application. Huazhong University of Science and Technology
Han J, Huang Y, Cercone N, Fu Y (1996) Intelligent query answering by knowledge discovery techniques. IEEE Trans Knowl Data Eng 8(3):373–390
Jia Wang et al (1997) Color clustering techniques for color content-B image retrieval from image database. In: Proceedings of the international conference on multimedia computing and systems, IEEE, vol 7, pp 442–449
Wei Na, Geng Guohua, Zhou Mingquan (2005) The use of Gabor filters for the content based image retrieval. Comput Eng 4:10–11
Xu Jie, Shi Pengfei (2003) The content based image retrieval technology. China J Image Graph 9:810–816
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this paper
Cite this paper
Gao, L., Wang, S., Chen, J. (2014). Multimedia Information Technology Application in Image Retrieval. In: Zhong, S. (eds) Proceedings of the 2012 International Conference on Cybernetics and Informatics. Lecture Notes in Electrical Engineering, vol 163. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3872-4_139
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3872-4_139
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3871-7
Online ISBN: 978-1-4614-3872-4
eBook Packages: EngineeringEngineering (R0)