Content based Image Indexing & Retrieval

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2015 by IJCOT Journal
Volume - 5 Issue - 1
Year of Publication : 2015
Authors : Pratap Singh Patwal , Dr. A.K. Srivastava
  10.14445/22492593/IJCOT-V16P305

MLA

Pratap Singh Patwal , Dr. A.K. Srivastava "Content based Image Indexing & Retrieval", International Journal of Computer & organization Trends (IJCOT), V5(1):26-31 Jan - Feb 2015, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract—Multimedia (Images) are being generated at an enormous rate by sources such as defense and civilian satellites, biomedical imaging, military reconnaissance and surveillance flights and home entertainment systems, scientific experiments, fingerprinting and mug-shot-capturing devices. For example, National Aeronautics and Space Administration (NASA) Earth Observing System will produce about 1 TB of image data per day when completely operational. A content- based image retrieval (CBIR) system is required to efficiently and competently use information from these image data sets. Such a system that helps users who is unfamiliar with the database can retrieve relevant images based on their contents.

References-

1. W. Grosky and R. Mehrotra, Guest Editors, Special Issue on “Image Database Management”, Computer, Vol. 22, No. 12, Dec. 1989.
2. Smith, J. and Chang, S. [1995] In Proc. IEEE Int. Conf. on Image Proc., pages 528-531, 1995. URL: citeseer.ist.psu.edu/smith95single.html
3. A.D. Narasimhalu, Guest Editor, Special Issue on Content-Based Reaieva1, ACMMultimedia Systems, Vol. 3, No. 1, Feb. 1995.
4. A. Ralescu and R. Jain, Guest Editors, Special Issue on “Advances in Visual Information Management Systems, Intelligent information Systems”, Vol. 3, No. 3, July 1994.
5. V. Gudivada, V. Raghavan, and K. Vanapipat, “A Unified Approach to Data Modeling and Retrieval for a Class of Image Database Applications,” in Multimedia Database Systems: Issues and Research Directions, S. Jajodia and V. Subrahmanian, Eds., Springer-Verlag, New York, 1995.
6. G. Salton, Automatic Text Processing, Addison-Wesley, Reading, Mass., 1989.
7. G. Jung and V. Gudivada, “Adaptive Query Reformulation in Attribute-Based Image Retrieval,” in Third Golden West Int‘l Conf. Intelligent System, Kluwer Academic Publishers, Boston, Mass., 1994, pp. 763-774.
8. Jun Yue, Zhenbo Li, Lu Liu, Zetian Fu “Content-based image retrieval using color and texture fused features” ELSEVIER Mathematical and Computer Modelling, Volume 54, Issues 3–4, August 2011, Pages 1121-1127
9. Rui, et al. - 1998 “Supporting ranked boolean similarity queries in mars – Ortega”
10. Del Bimbo, A. (1999). Visual Information Retrieval, Morgan Kaufmann, San Francisco,CA,
11. A. Gupta and R. Jain “Visual Information Retrieval” ACM,40.
12. Guttman, A. “R-tree: a dynamic index structure for spatial searching”. In In Proc ACM SIGMOD.
13. G. Pass, R. Zabih, and J. Miller (1996). “Comparing Images Using Color Coherence Vectors”. Proc. ACM Conference on Multimedia.
14. J. Dowe (1993). “Content-Based Retrieval in Multimedia Imaging”. Proc. SPIE.
15. Beckmann, N., Kriegel, H. P., Schneider, R., and Seeger, B. (1990).
“The R* tree: an efficient and robust access method for points and rectangles”. In In Proc ACM SIGMOD.
16. Charikar, M., Chekur, C., Feder, T., and Motwani, R. (1997). Incremental clustering and dynamic information retrieval. In In Proc of the 29th Annual ACM.
17. Duda, R. O. And Hart, P. E. “Pattern classification and scene analysis”. John Wiley and Sons, Inc.
18. Equitz, W. And Niblick, W. “Retrieving images from a database using a texture - algorithm for the cubic system”. Technical report, IBM.
19. Faloutsos, C., Flickner, M., Niblick, W., Petkovic, D., Equitz, W., and Barber, R. (1993). EÆcient and effective querying by image content. Technical report, IBM.
20. Gonzalez, R. C. And Woods, R. E. (1993). Digital Image Processing. Addison-Wesley Publishing Company, Inc., 3 edition.
21. Zhang, H. and Zhong, D. "A scheme for visual feature based image retrieval ", Proc. SPIE storage and retrieval for image and video databases
22. Hu, M. K. “Visual pattern recognition by moment invariants, computer methods in image analysis”. In IRE Transactions on Information Theory, volume 8.
23. Indrawan, M. “A Framework for Information Retrieval based on Bayesian Networks”. PhD thesis, Computer Science and Software Engineering. Monash
24. K.Prashanti Jasmine and P.Rajesh Kumar “Integration Of HSV Color Histogram And LMEBP Joint Histogram For Multimedia Image Retrieval” Intelligent computing, networking and informatics, advances in intelligent system and computing 243, DOI 101007/978-81-322-1665-0_76 Springer India 2014.
25. Swain, M. and Ballard, D. [1990]. Indexing via color histograms, AAAI-91 DARPA Image Understanding Workshop, p. 623630.
26. Patil, P.B. “Content Based Image Retrieval with Relevance Feedback Using Riemannian Manifolds” Signal and Image Processing (ICSIP), 2014 Fifth International Conference on Date of Conference:8-10 Jan. 2014 Page(s): 26 - 29 INSPEC Accession Number:14199015 Conference Location: Jeju Island DOI:10.1109/ICSIP.2014.9 Publisher: IEEE.
27. Vasconcelos, Lippman – 2000 “A probabilistic architecture for content-based image retrieval”.
28. H.Tamura, S. Mori, and T. Yamawaki. “Texture features corresponding to visual perception”.IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-8, no. 6, 1978, 460.

Keywords-
Content-Based Image Retrieval (CBIR), Web Service, Image Similarity, Information Visualization. TB (TeraByte).