Definition:Image retrieval techniques integrate both low-level visual features, addressing the more detailed perceptual aspects, and high-level semantic features underlying the more general conceptual aspects of visual data.
The emergence of multimedia technology and the rapid growth in the number and type of multimedia assets controlled by public and private entities, as well as the expanding range of image and video documents appearing on the web, have attracted significant research efforts in providing tools for effective retrieval and management of visual data. Image retrieval is based on the availability of a representation scheme of image content. Image content descriptors may be visual features such as color, texture, shape, and spatial relationships, or semantic primitives.
Conventional information retrieval is based solely on text, and these approaches to textual information retrieval have been transplanted into image retrieval in a variety of ways, including the...
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References
L.D.F. Costa and R.M. Cesar, Jr., “Shape Analysis and Classification: Theory and Practice,” CRC Press, 2000.
M. Flickner, H. Sawhney, W. Niblack, et al., “Query by Image and Video Content: The QBIC System,” IEEE Computer, Vol. 28, No. 9, September 1995, pp. 23–32.
W.I. Grosky, “Multimedia Information Systems,” IEEE Multimedia, Vol. 1, No. 1, Spring 1994, pp. 12–24.
M.L. Kherfi and D. Ziou, “Image Retrieval From the World Wide Web: Issues, Techniques, and Systems,” ACM Computing Surveys, Vol. 36, No. 1, March 2004, pp. 35–67.
O. Marques and B. Furht, “Content-Based Image and Video Retrieval,” Springer, 2002.
V. Ogle and M. Stonebraker, “Chabot: Retrieval from a Relational Database of Images,” IEEE Computer, Vol. 28, No. 9, September 1995, pp. 40–48.
Y. Rui, R.S. Huang, M. Ortega, and S. Mehrotra, “Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 8, No. 5, September 1998, pp. 644–655.
A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, December 2000, pp. 1349–1380.
R.C. Veltkamp and M. Tanase, “Content-Based Image Retrieval Systems: A Survey,” http://www.aa-lab.cs.uu.nl/cbirsurvey/cbir-survey/index.html.
C. Wang and X.S. Wang, “Indexing Very High-Dimensional Sparse and Quasi-Sparse Vectors for Similarity Searches,” The VLDB Journal, Vol. 9, No. 4, April 2001, pp. 344–361.
I.H. Witten, A. Moffat, and T.C. Bell, “Managing Gigabytes: Compressing and Indexing Documents and Images (2nd Edition),” Morgan Kaufmann, 1999.
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Grosky, W.I. (2006). Image Retrieval. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/0-387-30038-4_99
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DOI: https://doi.org/10.1007/0-387-30038-4_99
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