Rotation Invariant Content-Based Image Retrieval System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-17 Number-9
Year of Publication : 2014
Authors : P. Vijaya Bharati, A.Rama Krishna


P. Vijaya Bharati, A.Rama Krishna "Rotation Invariant Content-Based Image Retrieval System", International Journal of Engineering Trends and Technology (IJETT), V17(9),429-438 Nov 2014. ISSN:2231-5381. published by seventh sense research group


The emergence of multimedia technology and the rapid growth in the number and type of multimedia assets controlled by several entities, yet because the increasing range of image and video documents showing on the Internet, have attracted vital analysis efforts in providing tools for effective retrieval and management of visual data. So the need for image retrieval system arose. Out of many existing systems “ROTATION INVARIANT CONTENT-BASED IMAGE RETRIEVAL SYSTEM” is the most efficient and accurate one. Effective texture feature is an essential component in any CBIR system. In the past, spectral features like Gabor and Wavelet have shown superior retrieval performance than most statistical and structural options. Recent researches on multi-resolution analysis have found that curvelet captures texture properties like curves, lines and edges with additional accuracy than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can degrade its retrieval performance considerably, particularly in cases where there are many similar images with different orientations. We analyses the curvelet transform and derives a useful approach to extract rotation invariant curvelet features. The new system which uses curvelet transform for extracting texture features includes rotation invariant.


1. F. Long, et al., ‘Fundamentals of Content-based Image Retrieval,” in Multimedia Information Retrieval and Management, D. Feng Eds, Springer, 2003.
2. Gajanand Gupta, ‘Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter’, in International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-5, November 2011
3. S. Bhagavathy and K. Chhabra, “A Wavelet-based Image Retrieval System,” Technical Report—ECE278A, Vision Research Laboratory, University of California, Santa Barbara, 2007.
4. J. Starck, et al., “The Curvelet Transform for Image Denoising,” IEEE Trans. on Image Processing, 11(6), 670-684, 2002.
5. Digital Image Processing Rafel C.Gonzalez and Richard E.Woods Addison Wesley.
6. Digital Image Processing using MATLAB, Gonzalez and Woods.
7. Fundamentals of Digital Image Processing, Anol K Jain, Pearson.
8. Digital Image Processing and Analysis, B.Chanda & D Dutta majumder, Pearson.
9. Barbeau Jerome, Vignes-Lebbe Regine, and Stamon Georges, “A Signature based on Delaunay Graph and Co-occurrence Matrix,” Laboratoire Informatique et Systematique, University of Paris, Paris, France, July 2002, Foundation :\barbeau.pdf

Texture features, Color features, Shape features, Rotation Invariant, Gabor Filters, Wavelets