Main Article Content

Ravinder Kumar
Brajesh Kr. Singh


Due to increase in multimedia and internet technology day-by-day the Content Based Image Retrieval(CBIR) is an attractive research area for computer vision researcher since last decade. There is various model of CBIR have been proposed for retrieving images from huge database. In this work, we present an empirical analysis of CBIR model using Gabor feature descriptors in MATLAB. The similarity is measure by Euclidian distance method between query image and data base image. The efficiency of the CBIR Model can be calculating using both precision and recall and all the experimental results shows that system performs well on five different standard datasets.


Download data is not yet available.

Article Details

Author Biography

Ravinder Kumar, HMRITM, Affiliated with GGSIPU, DELHI

Ravinder Kumar received Ph. D. in IT from GGSIP University, Delhi in 2013 and M. Tech. degree in Computer Science & Engineering in 1998 from GJ University of Science and Technology, Hisar, India. Since 1999, he has been with the University School of ICT, GGSIP University, Delhi. Currently, he is Professor and Head, Department of CSE with HMR Institute of Technology and Management Delhi, India. His research interest is in the image processing and biometrics.


] R. Gonzales, R. E. Woods, “Digital Image Processing,†2nd Ed., New Jersey Prentice Hall, 2002.

Gevers and A. Smeulders, “Pictoseek: Combining color and shape invariant features for image retrieval,†IEEE Trans. Image Processing, vol. 9, no. 1, pp.102– 119, Nov. 2000

A. Ouyang, and Y. Tan, “A novel multi-scale spatial-color descriptor for content-based image retrieval,†Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, Mexico, August 2002, vol. 3, pp. 1204-1209.

H. Yu, M. Li, H. Zhang, and J. Feng, “Color texture moments for content-based image retrieval,†Proceedings of the International Conference on Image Processing, Rochester, New York, USA S,ptember 22-25, 2002, vol. 3, pp. 929-932.

T. Gevers, and H. Stokman, “Classifying color edges in video into shadow-geometry, highlight, or material transitions,†IEEE Transactions on Multimedia, vol. 5, no. 2, pp. 237-243, Sep. 2003.

H. Guan, and S. Wada, “Flexible color texture retrieval method using multi-resolution mosaic for image classification,†Proceedings of the 6th International Conference on Signal Processing, vol. 1, pp. 612-615, Feb. 2002.

H Moghaddam, T. Khajoie, and A. Rouhi, “A new algorithm for image indexing and retrieval using wavelet correlogram,†Proceedings of the International Conference on Image Processing, vol. 3, pp. 497-500, May 2003.

H.B. Kekre, “Survey of CBIR Techniques and Semanticsâ€, International Journal of Engineering Science and Technology (IJEST), ISSN: 0975-5462 Vol. 3 No. 5, May 2011.

M. Stricker and M. Orengo, “Similarity of color imagesâ€, In SPIE Conference on Storage and Retrieval for Image and Video Databases III, volume 2420, pages 381392, Feb. 1995.

J. Huang, et al., "Image indexing using color correlogram," IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 762-768, Puerto Rico, June 1997.

Haralick, K. Shanmugam, and I. Dinstein. “Texture Features for ImageClassification,†IEEE Trans. on Systems, Man and Cybernetics, SMC, vol.3, no 6, pp. 610–621, Nov. 1973.

J. E. Gary, and R. Mehrotra, "Shape similarity-based retrieval in image database systems," Proc. Of SPIE, Image Storage and Retrieval Systems, Vol. 1662, pp. 2-8, 1992.

W. I. Grosky, and R. Mehrotra, "Index based object recognition in pictorial data management," CVGIP, Vol. 52,

No. 3, pp. 416-436, 1990.

H. V. Jagadish, "A retrieval technique for similar shapes," Proc. of Int. Conf. on Management of Data, SIGMOID’91, Denver, CO, pp. 208-217, May 1991.

E. M. Arkin, L.P. Chew, D..P. Huttenlocher, K. Kedem, and J.S.B. Mitchell, "An efficientlycomputable metric for comparing polygonal shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 209-226, 1991.

S. Sclaroff, and A. Pentland, "Modal matching for correspondence andrecognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 17, No. 6, pp. 545-561, June 995.

K. Arbter, W. E. Snyder, H. Burkhardt, and G. Hirzinger, "Application of affine-invariant Fourier descriptors to recognition of 3D objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 640-647, 1990.

M. K. Hu, "Visual pattern recognition by moment invariants," in J. K. Aggarwal, R. O. Duda, and A. Rosenfeld, Computer Methods in Image Analysis, IEEE computer Society, Los Angeles, CA, 1977.

Long, H. Zhang, H. Dagan, and D. Feng, “Fundamentals of contentbased image retrieval,†in D. Feng, W. Siu, H. Zhang (Eds.):“Multimedia Information Retrieval and Management. Technological Fundamentals and Applications,†Multimedia Signal Processing Book, Chapter 1, Springer-Verlag, Berlin Heidelberg New York, 2003, pp.1-26.

Qian, S. Sural, Y. Gu, and S. Pramanik, “Similarity between Euclidean and cosine angle distance for nearest neighbor queries,†Proceedings of ACM Symposium on Applied Computing, vol. 12, no. 22, pp. 1232-1237, 2004.

Kumar, R., Chandra, P., & Hanmandlu, M. (2013, December). Fingerprint matching using rotational invariant image based descriptor and machine learning techniques. In Emerging Trends in Engineering and Technology (ICETET), 2013 6th International Conference on (pp. 13-18). IEEE.

Kumar, Ravinder, Pravin Chandra, and Madasu Hanmandlu. "Rotational invariant fingerprint matching using local directional descriptors." International Journal of Computational Intelligence Studies 3, no. 4 (2014): 292-319.