Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : P Gayathri | K Sureshkumarunnamed

Volume 03 Issue 06 2016

ISSN no:  2348-3121

Page no: 214-217

Abstract – Content-Based Image Retrieval (CBIR) system is rising as a crucial analysis space wherever the users will search and retrieve pictures supported by the properties like shape, color and texture from the image information. Typically texture-based image retrieval is taken into account as a clever image of coarseness, dissimilarity and roughness however there’s a lot of texture data within the edge image. This paper projected a unique approach to retrieve pictures by texture, color and SIFT. Moreover, the digital image is employed to represent the native characteristics of the image for increasing the accuracy of the retrieval system. Because the experimental results indicated, the projected technique so outperforms different schemes in terms of retrieval accuracy and class retrieval ability.

Keywords – content-based image retrieval (CBIR), HSV color house, GLCM, LBP, SIFT


  1. T. Ojala, M.Pietikainen, and T..Maenpa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE.Trans. Pattern anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, jul.2002.
  2. M. Guo, H. Prasetyo, and. J. H. Chen, “Content-based image retrieval using error diffusion block truncation coding features,”IEEE. Trans.Circuitssys. Videotech., 2014.
  3. Cao Li Hua, Liuwei, Li Guohui, “Research and implementation of an image retrieval algorithm based on multiple dominant colors,” journal of computer research & development, vol.36, no.1, 1999, pp.96–100.
  4. X.Tan, and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE. Trans. Image proc., vol. 19, no. 6, pp. 1635-1650, 2010.
  5. M.K. Mandal, T. Aboulnasr, and S. Panchanathan,, “image indexing using moments and wavelets”,IEEEtransactionson consumer electronics, vol. 42, no. 3, august 1996.
  6. T. Hamano, “A similarity retrieval method for image databases using simple graphics,” proc. Of IEEEworkshop on languages forautomation, symbiotic and intelligent robotics, pp. 149-154,university of maryland, august 29-33, 1988.
  7. T. kanungo , Mount , S.Netanyahun , Piatko C, Wu ay, “an eficient k — means clustering algorithm :analysis and implementation”, IEEE trans on pattern analysis and machine intelligence, 2002, 24(7): 881-892.
  8. Kabbai, M. Abdellaoui, A. Douik , “Hybrid classifier using sift descriptor”, in proc. Of IEEE international conference on control, decision and information technologies , 2013, pp. 388 – 392.
  9. B.S.Manjunath, Wyma, “Texture feature for browsing and retrieval of image data,” IEEE transaction on pami, vol.18, no.8, 1996, pp. 837–842.
  10. J. Yu, Z. Qin, T. Wan, X. Zhang, “Feature integration analysis of bag-of-features model for image retrieval”,neurocomputing,2013, vol.120, pp. 355–364.
  11. Z. Liang, H. Fu, Z. Chi, D. Feng, “Salient-sift for image retrieval”,advanced concepts for intelligent vision systems, 2010, vol. 6474,pp. 62–71.
  12. Liang and .al, “image matching based on orientation–magnitudehistograms and global consistency”, pattern recognition, 2012, vol.45, issue.10, pp. 3825–3833.
  13. z. Guo, l. Zhang, and d. Zhang, “rotation invariant texture classificationusinglbp variance with global matching,” pattern recognition, vol. 43,pp. 706-716, 2010.
  14. M. Subramanian, R.P. Maheswari, and r. Balasubramanian, “localmaximum edge binary patterns: a new descriptor for image retrieval and object tracking,” signal processing, vol. 92, pp. 1467-1479, 2012.
  15. B. Zhang, Y.Gao, S. Zhao, and J. Liu, “Local derivative pattern versuslocal binary pattern: face recognition with high-order local pattern descriptor,” IEEE trans. Image process.vol. 19, no. 2, pp.533-544, 2010.
  16. Q. Zhao, J. Yang, and H. Liu, Stone images retrievalbased on color histogram,” proceedings of IEEE international conference on image analysis and signal processing 2009, iasp2009, pp. 157-161, April 2009