Performance Evaluation of Content Based Image Retrieval Using Indexed Views
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Performance Evaluation of Content Based Image Retrieval Using Indexed Views

Authors: Tahir Iqbal, Mumtaz Ali, Syed Wajahat Kareem, Muhammad Harris

Abstract:

Digital information is expanding in exponential order in our life. Information that is residing online and offline are stored in huge repositories relating to every aspect of our lives. Getting the required information is a task of retrieval systems. Content based image retrieval (CBIR) is a retrieval system that retrieves the required information from repositories on the basis of the contents of the image. Time is a critical factor in retrieval system and using indexed views with CBIR system improves the time efficiency of retrieved results.

Keywords: Content based image retrieval (CBIR), Indexed view, Color, Image retrieval, Cross correlation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1093792

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References:


[1] Kato T, Kurita T, Otsu N, Hirata K. A sketch retrieval method for full color image database-query by visual example. In Proc. IntConfon Pattern Recognition Aug-3, 1992; 530-533W.-K. Chen, Linear Networks and Systems (Book style).Belmont, CA: Wadsworth, 1993, pp. 123–135.
[2] Huiyu Zhou, Abdul H. Sadka, Muhammad R. Swash, Jawid Aziz and Abubakar S. Umar Content Based Image Retrieval and Clustering: A Brief Survey.
[3] Jain R. Workshop report: NSF workshop on visual information management system. In storage and retrieval for image and video databases. In: Niblack WR, Jain RC, Eds. Proc. SPIE 1908, 1993; 198-218.
[4] Content Based Image Retrieval (CBIR) Using Materialized Views Mumtaz Ali, Awais Adnan, MuhammdSaqib and Zahidullah.
[5] D. Warehousing and J. Notices, "Data Engineering,” vol. 18, no. 2, 1996.
[6] Optimizing Queries Using Materialized Views: A Practical, Scalable Solution Jonathan Goldstein and Per-Åke Larson ACM SIGMOD 2001 May 21-24, Santa Barbara, California USA Copyright 2001 ACM 1-58113-332-4/01/05.
[7] John Eakins, Content-based Image Retrieval Margaret Graham University of Northumbria at Newcastle.
[8] Flickner M, Sawhney H, Niblack W, et al. Query by image and video content: The QBIC systComput 1995; 28: 23-32.
[9] Pentland A, Picard R, Scaroff S. Photobook: Content-based manipulation for image databases. Int J Comput Vision 1996; 18: 233-254.
[10] Gupta A, Jain R. Visual information retrieval. Commun ACM 1997; 40: 70-79.
[11] Smith J, Chang S-F. Visualseek: A fully automated content-based image query system. In multimedia’96: Proc. of the Fourth ACM IntConf on Multimedia 1996; 87-98.
[12] Ma W, Manjunath B. Netra: A toolbox for navigating large image databases. In Proc. IntConf on Image Process 1997; 1: 568-571.
[13] Wang JZ, Li J, Wiederhold G. Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Machine Intell 2001; 23: 947-963.
[14] Jin J, Kurniawati R, Xu G, Bai X. Using browsing to improve content based image retrieval .J Visual Commun Image Represent2001;12:123-135.
[15] Awais Adnan, Muhammad Nawaz, Sajid Anwar, Tamleek Ali, Muhammad Ali. Object Identification with Color, Texture, and Object Correlation In CBIR System, World Academy of Science, Engineering and Technology 64 2010