Abstract

This paper presents novel techniques for image retrieval using the clustering features extracted from images based on Row Mean Clustering, Column Mean clustering, Row Mean DCT Clustering ,Column Mean DCT Clustering, Row Mean Wavelet Clustering and Column Mean Wavelet Clustering. The proposed techniques are compared with well known traditional technique such as Hierarchical Clustering. Hierarchical clustering starts by calculating the Euclidean distance measure for all patterns in data set, which is not required to calculate in proposed techniques. Hence number of clusters used for comparison of proposed techniques is less as compared to existing technique (Hierarchical Clustering). All the CBIR techniques are implemented on a database having 665 images spread across 31 classes. The results of proposed techniques have shown performance improvement (higher precision and Recall) as compared to existing technique at reduced computations.

References

• Guoping Qiu," Color Image Indexing Using BTC,"IEEE Transactions on Image Processing, VOL. 12, NO. 1, pp. 93-101, January 2003.
• B. G. Prasad, K. K. Biswas, and S. K. Gupta," Region –based image retrieval using integrated color, shape, and location index," computer vision and image understanding, October 2003.
• Minh N. Do, Member, IEEE, and Martin Vetterli, Fellow, IEEE," Wavelet-Based Texture Retrieva Using Generalized Gaussian Density and Kullback- Leibler Distance," IEEE Transactions On Image Processing, VOL. 11, NO. 2, February 2002
• Michael Eziashi Osadebey ," Integrated content – based image retrieval using texture , shape and spatial information ",Master Thesis Report in Media Signal Processing , Department of Applied Physics and Electronics, Umea University, Umea Sweden
• Rajashekhara," Novel Image Retrieval Techniques domain specific approaches," Ph. D. Thesis Department of Electrical Engineering Indian Institute of Technology – Bombay, 2006.
• Mrs Monika Jain, Dr. S. K. Singh ," A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets", International Journal of Managing Information Technology (IJMIT) Vol. 3, No. 4, November 2011
• K. Velmurugan, Lt. Dr. S. Santosh Baboo ," Image Retrieval Using Harris Corners and Histogram of Oriented Gradients," International Journal of Computer Applications (0975- 8887)Volume 24, No. 7, June 2011
• Junqiu Wang and Hongbin Zha , Roberto Cipolla," Combining Interest Points and Edges for Content-based Image Retrieval," IEEE Journal, June 8,2010.
• Neetu Sharma. , Paresh Rawat and jaikaran Singh. ," Efficient CBIR Using Color Histogram Processing, " Signal & Image Processing : An International Journal(SIPIJ) Vol. 2, No. 1, March 2011.
• Minakshi Banerjeea, MalayK. Kundua,b, Pradipta Majia,b ," Content-based image retrieval Using visually significant point features,"Elsevier , Fuzzy Sets and Systems 160 (2009) 3323–3341
• Swapna Borde , Dr. Udhav Bhosle ," Image Retrieval Using Contourlet Transform," International Journal of Computer Applications (0975-8887),Volume 34-No. 5, November 2011.
• Swapna Borde , Dr. Udhav Bhosle ," Image Retrieval Using Steerable Pyramid," International Journal of Computer Applications (0975-8887),Volume 38-No. 7, January 2012.
• Yixin Chen, James Z. Wang, Robert Krovetz,"Content-Based Image Retrieval by Clustering
• P. Valarmathie, T. Ravichandran and K. Dinakaran ," Survey on Clustering Algorithms for Microarray Gene Expression Data", European Journal of Scientific Research ISSN 1450-216X Vol. 69 No. 1 (2012), pp. 5-20
• K. Mumtaz and Dr. K. Duraiswamy , A Novel Density based improved k-means Clustering Algorithm – Dbkmeans", International Journal on Computer Science and Engineering ISSN : 0975-3397 213 Vol. 02, No. 02, 2010, 213-218