The paper presents the Wavelet Pyramid based image retrieval techniques [1] using Haar transform. Here content based image retrieval (CBIR) is done using the image feature set extracted from Haar Wavelets applied on the image at various levels of decomposition. Here the database image features are extracted by applying Haar Wavelets on gray plane (average of red, green and blue) and color planes (red, green and blue components). The techniques Gray-Haar Wavelets and Color-Haar Wavelets are tested on image database having 11 categories with total 1000 images. Total 55 queries are fired on the database. The results show that precision and recall of Haar Wavelets are better than complete Haar transform based CBIR, which proves that Haar Wavelets gives better discrimination capability in image retrieval at higher query execution speed, per higher level Haar Wavelets. Color-Haar Wavelets based CBIR have greater precision and recall than Gray-Haar Wavelets based CBIR. The Haar Wavelets level-5 outperforms other Haar Wavelets, because the higher level Haar Wavelets are giving very coarse color-texture features while the lower level are representing very fine color-texture features which are less useful to differentiate the images in image retrieval.

1. H.B.Kekre, Sudeep D. Thepade, “Image Retrieval using Color-Texture Features Extracted from Walshlet Pyramid”, ICGST International Journal on Graphics, Vision and Image Processing (GVIP), Volume 10, Issue I, Feb.2010, pp.9-18, Available online www.icgst.com/gvip/Volume10/Issue1/P1150938876.html.
2. H.B.Kekre, Sudeep D. Thepade, “Image Retrieval using Augmented Block Truncation Coding Techniques”, Proc. ACM Int. Conf. on Advances in Computing, Communication and Control (ICAC3-2009), 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg., Mumbai. Is uploaded and available online at ACM portal.
3. H.B.Kekre, Sudeep D. Thepade, “Rendering Futuristic Image Retrieval System”, Proc. National Conf. on Enhancements in Computer, Comm. and Info. Technology, EC2IT-2009,20-21 Mar 2009, K.J. Somaiya COE, Vidyavihar, Mumbai-77.
4. H.B.Kekre, Sudeep D. Thepade, “Color Traits Transfer to Grayscale Images”, IEEE –International Conference on Emerging Trends in Engineering and Technology, ICETET-2008, 16-18 July 2008, Raisoni College of Engineering, Nagpur. Is uploaded and available online at IEEE Xplore CSDL, ACM Portal
5. H.B.Kekre, Sudeep D. Thepade, “Image Blending In Vista Creation using Kekre’s LUV Color Space” , SPIT -IEEE Colloquium and International Conference, 04-05 Feb 2008 ,SPIT Andheri ,Mumbai.
6. K.-C. Liang and C. C. Kuo, "WaveGuide: A Joint Wavelet-Based Image Representation and Description System," IEEE Trans. on ImageProcessing, vol. 8, no. 11, pp.1619-1629, 1999
7. W. Y. Ma, B. S. Manjunath, "A comparison of wavelet features for texture annotation," Proc. of IEEE Int. Conf. on Image Processing, Vol. II, pp. 256-259, Washington D.C., Oct. 1995.
8. Hirata K. and Kato T. “Query by visual example – content-based image retrieval”, In Proc. of Third International Conference on Extending Database Technology, EDBT’92, 1992, pp 56-71.
9. Haar, Alfred, “Zur Theorie der orthogonalen Funktionen systeme”. (German), Mathematische Annalen, volume 69, No. 3, 1910, pp. 331–371.
10. Charles K. Chui, “An Introduction to Wavelets”, Academic Press, 1992, San Diego, ISBN 0585470901.
11. http://wang.ist.psu.edu/docs/related/Image.orig (last referred on June, 10th, 2009)
12. A. K. Jain , A. Vailaya, “Image Retrieval using Colour and Shape," In Proc. of 2nd Asian Conference on Computer Vision (ACCV-95), Singapore, 1995, pp. 529-533.
13. H.B.Kekre, Sudeep D. Thepade, “Ubicomp The Future of Computing Technology”, TechnoPath : Journal of Science Technology and Management, Volume 1, Issue 2, 2009.
14. Robert Li, Jung Kim, “Image Compression Using Fast Transformed Vector Quantization”, IEEE Applied Imagery Pattern Recognition Workshop, 2000 Proceedings, Volume 29, 2000, pp.141 – 145.
15. B.G.Prasad, K.K. Biswas, and S. K. Gupta, “Region –based image retrieval using integrated color, shape, and location index”, International Journal on Computer Vision and Image Understanding Special Issue: Colour for Image Indexing and Retrieval, Volume 94, Issues 1-3, April-June 2004, pp.193-233.
16. Minh N. Do, , and Martin Vetterli, , “Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance”, IEEE Transactions On Image Processing, Volume 11, Number 2, pp.146-158, February 2002.
17. A. Gupta, R. Bach, C. Fuller, A. Hampapur, B. Horowitz, R. Jain, C.F. Shu “The Virage image search engine: an open framework for image management” in Storage and Retrieval for Image and Video Databases IV, Proc SPIE Vol. 2670, pp 76-87, 1996.
18. M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. “Query by image and video content: The QBIC system,” IEEE Computer, vol. 28, pp. 23-32, 1995.
19. H.B.Kekre, Sudeep D. Thepade, “Boosting Block Truncation Coding using Kekre’s LUV Color Space for Image Retrieval”, WASET International Journal of Electrical, Computer and System Engineering (IJECSE), Volume 2, Number 3, Summer 2008. Available online at http://www.waset.org/ijecse/v2/v2-3-23.pdf
20. M. La Cascia, S. Sethi, S. Sclaroff. “Combining textual and visual cues for content-based image retrieval on the world wide web”, In IEEE Workshop on Content-based Access of Image and Video Libraries, pp 24–28, Santa Barbara, CA, June 1998.
21. Carson M. Thomas, S. Belongie, J. M. Hellerstein, and J. Malik, “Blobworld: a system for region-based image indexing and retrieval”, In Visual Information and Information Systems (VISUAL), LNCS 1614, pages 509–516, Amsterdam, The Netherlands, June 1999.
22. S. Sclaroff, L. Taycher, and M. La Cascia, “ImageRover: a content-based image browser for the world wide web”, In IEEE Workshop on Content-based Access of Image and Video Libraries, pages 2–9, San Juan, Puerto Rico, June 1997.
23. H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Image Retrieval using Color-Texture Features from DCT on VQ Codevectors obtained by Kekre’s Fast Codebook Generation”, ICGST International Journal on Graphics, Vision and Image Processing (GVIP), Volume 9, Issue V, Sept. 2009, pp. 1-8, Available onlineathttp://www.icgst.com/gvip/Volume9/Issue5
24. Zaher Al Aghbari and Ruba Al-Haj, “Building SSeg-Tree for Image Representation and Retrieval”, ICGST Int. Journal on Graphics, Vision and Image Processing (GVIP), Special Issue on Image Retrieval and Representation, Vol. 6, Year 2006, pp. 101-109.
25. M.Eisa, I.Elhenawy, A.E.Elalafi and H. Burkhardt, “Image Retrieval based on Invariant Features and Histogram Refinement”, ICGST Int. Journal on Graphics, Vision and Image Processing (GVIP) , Special Issue on Image Retrieval and Representation, Vol. 6, Year 2006, pp. 7-11.
26. H.B.Kekre, Sudeep D. Thepade, “Color Based Image Retrieval using Amendment Block Truncation Coding with YCbCr Color Space”, International Journal on Imaging (IJI), Volume 2, Number A09, Autumn 2009, pp.2-14. Available online at www.ceser.res.in/iji.html
27. H.B.kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Color-Texture Feature based Image Retrieval using DCT applied on Kekre’s Median Codebook”, International Journal on Imaging (IJI), Volume 2, Number A09, Autumn 2009,pp. 55-65. Available online at www.ceser.res.in/iji.html
28. Tuceryan M., Jain A.K.,“Texture Analysis Handbook of Pattern Recognition and Computer Vision (Eds. C.H.Chen, L.F.pau, P.S.P.Wang), 1994.

Content Based Image Retrival (CBIR) Haar Wavelets Haar wavelet Pyramid Color-texture