Email this article 
Open Access
Subscription or Fee Access
A Survey on Image Data Retrieval in Digital Libraries
Abstract
Recently major changes have been made in representing manual data into digital data, so the topic of digital libraries has become more popular and its research also have been carried out very actively in this area. Due to this rapid advancement of digital data, the scalability of the storage is also gradually increased. When scalability of the storage is increased, the retrieval of such data is very tedious process. The problem of retrieval and managing of such data has become very important for researchers who need efficient and simple access to the data in their specific applications. This paper presents a survey on image data retrieval in digital library techniques and their methods.
Keywords
References
Ze-Nian Li, Osmar R. ZaÏane, Bing Yan, "C-BIRD: Content Based Image Retrieval from Digital Libraries Using Illumination Invariance and Recognition Kernel, " dexa, pp. 361, 9th International Workshop on Database and Expert Systems Applications (DEXA'98), 1998.
Rasmussen, E. “Indexing images”, Annual Review of Information Science and Technology, 32, 169-196, 1997.
Lancaster, F. “Indexing and abstracting in theory and practice, 2nd edition”, Library Association, London, 1998
Cawkell, A. “Indexing collections of electronic images: A review”,British Library Research Review, 15, 1993.
Zheng, M. “Metadata elements for object description and representation:A case report from a digitized historical fashion collection project”,Journal of the American Society for Information Science, 50(13), 1193-1208, 1999.
Goodrum, A., & Martin, K. “Bringing fashion of the closet: Classification structure for the Drexel Historic Costume Collection”,Bulletin of the American Society for Information Science, Volume 25,Number 6, pp21-23, August/September, 1999.
Hourihane, C, “A selective survey of systems of subject classification”,Computers and the History of Art. 117-129, 1989.
Abby A. Goodrum, “Image Information Retrieval: An Overview of Current Research”, Special Issue on Information ScienceResearch,Volume 3 No 2. 63-67, 2000.
Shatford, S “Analyzing the subject of a picture: a theoretical approach”,Cataloging and Classification Quarterly, 6(3), 39-62, 1986.
Shatford-Layne, S “Some issues in the indexing of images”, Journal of the American Society of Information Science, 45(8), 583-588, 1994.
Turner, J “Representing and accessing information in the stock shot database at the National Film Board of Canada”, The Canadian Journal of Information Science v. 15 p 1-22, 1994.
Lawrence, S. & Giles, L “Accessibility of Information on the web:Nature”, vol. 400, 107-109, 1999.
C. S. McCamy, H. Marcus, and J. G. Davidson, “A color –rendition chart”, Journal of Applied Photographic Engineering 2(3), 1976.
M. Miyahara, “Mathematical transform of(r, g, b) color data to munsell(h, s, v) color data”, SPIE Visual Communication Image Process, 1001,1988.
J. Wang, W. -J. Yang, and R. Acharya, “Color clustering techniques for color-content-based image retrieval from image database”, in Proc.IEEE Conference on Multimedia Computing and Systems, 1997.
M. J. Swain and D. H. Ballard, “Color Indexing”, International Journal of Computer Vision, 7(1):11-32, 1991.
G. F. Sudha, “Relevance Feedback for Image Retrieval”IE (I) Journal-CP, Vol 89, 2008.
J. F. Canny, “A Computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 8, no 6,1986, p 679.
R. Gonzalez and R. Woods, “Digital Image Processing”, Addison Wesley Publication, 1992.
Person E, Fu K, “Shape discrimination using Fourier descriptors”. IEEE Transactions on System, Man and Cybernatics, SMC-7(3), 171-181,1977.
Yang HS, Lee SU, Lee KM, “Recognition of 2D object Contours using starting- point-independent wavelet Coefficient matching, Journal of VisualComm, Image Represent, 9(2), 171-181, 1998.
Abbasi S, Mokhtarian F, Kittler J, “Curvature scale space image in shape similarity retrieval”, Multimedia Syst, 7(6), 467-476, 1999.
A. Grace Selvarani and S. Annadurai, “Content Based Medical Image Retrieval System using Shape and Texture Features”, ICGST-BIME Journal, Vol 8, Issue 1, December2008.
M. Stricker and M. Orengo, “Similarity of Color Images”, In Proc.SPIE: Storage and Retrieval for Image and Video Databases, Vol. 2420,pp381-392, 1995.
H. Voorhees and T. Poggio, “Computing Texture Boundaries from Images, Nature, 333:364-367, 1988.
J. R. Simth, “Integrated Spatial and Feature Image System: Retrieval,Analysis and Compression”, PhD thesis, Columbia University, 1997.
Tamura, H et al (1978) “Textural features corresponding to visual perception” IEEE Transactions on Systems, Man and Cybernetics 8(6),460-472
Liu, F and Picard, R W (1996) “Periodicity, directionality and randomness: Wold features for image modelling and retrieval” IEEE Transactions on Pattern Analysis and Machine Intelligence 18(7), 722-733
Manjunath, B S and Ma, W Y (1996) “Texture features for browsing and retrieval of large image data” IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 837-842.
Ma W Y and Manjunath, B S (1998) “A texture thesaurus for browsing large aerial photographs” Journal of the American Society for Information Science 49(7), 633-648.
Kaplan, L M et al (1998) “Fast texture database retrieval using extended fractal features “in Storage and Retrieval for Image and Video Databases VI (Sethi, I K and Jain, R C, eds), Proc SPIE 3312, 162-173
Adjusting similarity measure [Rui et al. IEEE CSVT 8(5); Cox et al.IEEE Trans. IP 9(1)].
Support vector machine [Tong et al. ACM MM’2001].
A. Grace Selvarani and S. Annadurai, “Content Based Image Retrieval for Medical Images using Generic Fourier Descriptor”, Journal of Computational Intelligence in Bioinformatics, ISSN: 0973-385X, Vol1,Number1, (2008), pp 65-72.
A. Del Bimbo, “Visual Information Retrieval”, Morgan Kaufmann Publishers, Inc, San Francisco, CA, 1999, pp. 56-57 36.
Mohammed Eisa and Ibrahim Elhenawy and A. E. Elalfi and Hans Burkhardt, “Image Retrieval based on Invarian Features and Histogram Refinement”, ICGST International Journal on Graphics, Vision and Image Processing, March 2006, pp. 7-11.
M. Flickner, et al, “Query by Image and Video Content: The QBIC System, ”IEEE Computer, 28(9): 23-32, 1995.
A. Hampapur, A. Gupta, B. Horowitz, and C. F. Shu, “The Virage Image Search Engine: An Open Framework for Image Management”, in Storage and Retrieval for Image and Video Databases (SPIE), 1997,188-198.
Lehmann TM, Guld MO, Thies C, Fischer B, Sphzer K, Keysers D, etal, “Content-based image retrieval in medical applications”, Methods of Info in Med 2004; 73(1):1-23.
Thies C, Guld MO, Fischer B, Lehmann TM, “Content- based queries on the CasImage database with in the IRMA framework”, Lec Notes in Comp Sci 2005; 3491:781-92.
Antani S, Long LR, Thoma GR, “Content-based Image retrieval for large biomedical image archives. In: Proc 11th World Cong Medical Informatics: 2004. p. 829-33.
Long LR, Antani SK, Thoma GR, “Image Informatics at a national research center, CompMed Imaging &graphics 2005; 29:171-93.
Thoma GR, Long LR, Antani SK, Biomedical Imaging research and development: knowledge from images in the medical enterprise. Technical Report Lister Hill National Ctr for Biomedical Communications, US National Library of Medicine, NIH 2006; LHNCBC-TR-2006-002.
Petrakis, Euripidies G. M and C. Faloutsos, “ImageMap: An Image indexing Method Based on Spatial Similarity”, IEEE Trans on Knowledge and Data Eng., 14(5):979-987, 2002.
Chi-Ren Shyu, Carla E. Brodley, Avinash C. Kak, Akio Kosaka, “ASSERT: A Physician-in-the-Loop Content-Based Retrieval System for HRCT Image Databases”, Computer Vision and Image Understanding, Vol. 75, Nos. 1/2, July/August, pp. 111-132, 1999.
Wu, J K and Narasimhalu, A D, “Identifying faces using multiple retrievals. ”, IEEE Multimedia, 1994, 1(2), 27-38.
Ashley, W “What shoe was that? The use of computerised image database to assist in Identification. ” Forensic Science International, 1996, 82, 7-20.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.