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Image Retrieval using Quantized Local Binary Pattern

by P. Latha, V. Vijaya Kumar, A. Obulesu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 5
Year of Publication: 2016
Authors: P. Latha, V. Vijaya Kumar, A. Obulesu
10.5120/ijca2016912308

P. Latha, V. Vijaya Kumar, A. Obulesu . Image Retrieval using Quantized Local Binary Pattern. International Journal of Computer Applications. 155, 5 ( Dec 2016), 7-15. DOI=10.5120/ijca2016912308

@article{ 10.5120/ijca2016912308,
author = { P. Latha, V. Vijaya Kumar, A. Obulesu },
title = { Image Retrieval using Quantized Local Binary Pattern },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 5 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 7-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume155/number5/26599-2016912308/ },
doi = { 10.5120/ijca2016912308 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:00:26.987485+05:30
%A P. Latha
%A V. Vijaya Kumar
%A A. Obulesu
%T Image Retrieval using Quantized Local Binary Pattern
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 5
%P 7-15
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image retrieval is one of the main topics in the field of computer vision and pattern recognition. Local descriptors are gaining more and more recognition in recent years as these descriptors are capable enough to identify the unique features, which suitably and uniquely describe any image for recognition and retrieval. One of the popular and efficient frame works for capturing texture information precisely is the Local binary pattern (LBP). LBP descriptors perform well in varying pose, illumination and lighting conditions. LBP is a structural approach and plays significant role in wide range of applications. One of the disadvantages with LBP based frame work is its dimensionality. The dimensionality of LBP increases, if one increases the number of neighboring pixels. Further statistical approaches gained lot of significance in image retrieval and LBP based methods raises high dimensionality and complexity issues, in deriving statistical features. The present paper addresses these two issues by quantizing the LBP code, to reduce dimensionality and by deriving GLCM features on quantized LBP. The proposed method is experimented on Corel database and compared with other existing methods. The experimental results indicate the high retrieval rate by the proposed method over the existing methods.

References
  1. Y. Rui and T. S. Huang, Image retrieval: Current techniques, promising directions and open issues, J.. Vis. Commun. Image Represent., 10 (1999) 39–62.
  2. A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-based image retrieval at the end of the early years, IEEE Trans. Pattern Anal. Mach. Intell., 22 (12) 1349–1380, 2000.
  3. Alnihoud J., “Content-Based Image Retrieval System Based on Self Organizing Map, Fuzzy Color Histogram and Subtractive Fuzzy Clustering,” the International Arab Journal ofInformation Technology, vol. 9, no. 5, pp. 452-458, 2012.
  4. Jain A. and Farrokhnia F., “Unsupervised Texture Segmentation using Gabor Filters,” InProceedings of IEEE International Conferenceon Systems Man and Cybernetic, Los Angeles, USA, pp. 14-19, 1990.
  5. M. Kokare, B. N. Chatterji, P. K. Biswas, A survey on current content based image retrieval methods, IETE J. Res., 48 (3&4) 261–271, 2002.
  6. Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma, Asurvey of content-based image retrieval with high-level semantics, Elsevier J. Pattern Recognition, 40, 262-282, 2007.
  7. M. J. Swain and D. H. Ballar, Indexing via color histograms, Proc. 3rd Int. Conf. Computer Vision, Rochester Univ., NY, (1991) 11–32.
  8. M. Stricker and M. Oreng, Similarity of color images, Proc. SPIE, Storage and Retrieval for Image and Video Databases, (1995) 381–392.
  9. G. Pass, R. Zabih, and J. Miller, Comparing images using color coherence vectors, Proc. 4th ACM Multimedia Conf., Boston, Massachusetts, US, (1997) 65–73.
  10. J. Huang, S. R. Kumar, and M. Mitra, Combining supervised learning with color correlograms for content-based image retrieval, Proc. 5th ACM Multimedia Conf., (1997) 325–334.
  11. Z. M. Lu and H. Burkhardt, Colour image retrieval based on DCT domain vector quantization index histograms, J. Electron. Lett., 41 (17) (2005) 29–30.
  12. Chen Y., Wang J., and Krovetz R., “Content Based Image Retrieval by Clustering,” in Proceedings of 5th International ACM Workshop on Multimedia Information Retrieval, pp. 193-200, 2003.
  13. Monika J. and Singh S., “An Experimental Study on Content Based Image Retrieval Based on Number of Clusters using Hierarchical Clustering Algorithm,” International Journal of SignalProcessing, Image Processing and PatternRecognition, vol. 7, no. 4, pp. 105-114, 2014.
  14. Pavani P. and Prabha S., “Content Based Image Retrieval using Machine Learning Approach,” in Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications, vol. 247, pp. 173-179, 2013.
  15. Y Venkateswarlu, B Sujatha, V Vijaya Kumar, Image classification based on center symmetric fuzzy texture unit matrix, International journal of scientific & engineering research, Vol.13, No.11, pp.5-11, Oct-2012, ISSN 2229-5518.
  16. V.Vijaya Kumar, B. Eswar Reddy, U.S.N. Raju, K. Chandra Sekharan,An innovative technique of texture classification and comparison based on long linear patterns, Journal of computer science, Science publications, Vol.3, No.8, pp.633-638, Aug-2007, ISSN: 1552-6607.
  17. V Vijaya Kumar, U S N Raju, K Chandra Sekaran, V V Krishna, Employing long linear patterns for texture classification relying on wavelets, ICGST-Graphics, vision and image processing (ICGST-GVIP), Vol.8,No.5, pp. 13-21, Jan-2009, ISSN: 1687-398X
  18. V. Vijaya Kumar, K. Srinivasa Reddy, V. Venkata Krishna , Face Recognition Using Prominent LBP Model; International Journal of Applied Engineering Research , Vol. 10, Issue 2, pp. 4373-4384, 2015, ISSN: 0973-4562
  19. M. Chandra Mohan,V. VijayaKumarU.S.N. Raju,New face recognition method based on texture features using linear wavelet transforms, International journal of computer science and network security (IJCSNS), Vol.9 No.12, pp.223-230, Dec-2009, ISSN: 1738-7906.
  20. P. Chandra SekharReddy,B. EswaraReddy,V. Vijaya Kumar, Fuzzy based image dimensionality reduction using shape primitives for efficient face recognition, ICTACT- Journal on image and video processing, Vol. 04, NO. 02, pp. 695-701, Nov-2013, ISSN: 0976-9102.
  21. V Vijaya Kumar, GortiSatyanarayaMurty, PS V V S R Kumar, Classification of facial expressions based on transitions derived from third order neighborhood LBP, Global journal of computer science and technology graphics & vision (GJCST), Vol.14, N0.1, pp. 1-12, Jan-2014, ISSN: 0975-4350.
  22. G S Murty ,J SasiKiran , V.Vijaya Kumar, Facial expression recognition based on features derived from the distinct LBP and GLCM, International journal of image, graphics and signal processing (IJIGSP), , Vol.2, No.1, pp. 68-77,2014, ISSN: 2074-9082.
  23. V.VijayaKumar,SakaKezia, I.SantiPrabha,A new texture segmentation approach for medical images, International journal of scientific & engineering research, Vol. 4, No.1, pp.1-5, January-2013, ISSN: 2229-5518.
  24. V.Vijaya Kumar, U.S.N. Raju, M. Radhika Mani, A. NarasimhaRao,Wavelet based texture segmentation methods based on combinatorial of morphological and statistical operations, International journal of computer science and network security (IJCSNS), Vol.8, No.8, pp.176-181, Aug-2008, ISSN : 1738-7906.
  25. SakaKezia, I.SantiPrabha, V.VijayaKumar,A color-texture based segmentation method to extract object from background, International journal image, and graphics and signal processing, Vol. 5, No. 3, pp.19-25, March-2013, ISSN: 2074- 9082.
  26. NG rao,DVVKDPSVS Rao, Novel Approaches of evaluating Texture Based Similarity Features for Efficient Medical Image Retrieval System, International Journal Of Computer Applications, ISSN-0975-8887 20(7),20-26
  27. A Obulesu,JSKiran, VV Kumar, Facial image retrieval based on local and regional features, IEEE- 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 29-31 Oct. 2015, Pp:841 – 846
  28. V.Vijaya Kumar, A. Srinivasa Rao, YK Sundara Krishna, Dual Transition Uniform LBP Matrix for Efficient Image Retrieval, I.J. Image, Graphics and Signal Processing, 2015, 8, 50-57.
  29. V. Vijaya Kumar, N. Gnaneswara Rao, A.L.Narsimha Rao, And V.Venkata Krishna4,IHBM: Integrated histogram bin matching for similarity measures of color image retrieval, International journal of signal processing, image processing and pattern recognition, Vol. 2, No.3, pp. 109-120, Sep-2009, ISSN: 2005-4254.
  30. He X., Ma W., and Zhang H., “Learning an Image Manifold for Retrieval,” in Proceedings ofACM International Conference on Multimedia,New York, USA, pp. 17-23, 2004.
  31. Chen Y., Wang J., and Krovetz R., “An Unsupervised Learning Approach to Content-Based Image Retrieval,” in proceeding of 7th International Symposium on SignalProcessing and its Applications, pp. 197-200, 2003.
  32. Kim D. and Chung C., “Qcluster: Relevance Feedback using Adaptive Clustering for Content Based Image Retrieval,” in Proceedings of the ACM SIGMOD'03, San Diego, USA, pp. 599-610, 2003.
  33. J. R. Smith and S. F. Chang, Automated binary texture feature sets for image retrieval, Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Columbia Univ., New York, (1996) 2239–2242.
  34. H. A. Moghaddam, T. T. Khajoie, A. H Rouhi and M. Saadatmand T., Wavelet Correlogram: A new approach for image indexing and retrieval, Elsevier J. Pattern Recognition, 38 (2005) 2506-2518.
  35. H. A. Moghaddam and M. Saadatmand T., Gabor wavelet Correlogram Algorithm for Image Indexing and Retrieval, 18th Int. Conf. Pattern Recognition, K.N. Toosi Univ. of Technol., Tehran, Iran, (2006) 925-928.
  36. A.Ahmadian, A. Mostafa, An Efficient Texture Classification Algorithm using Gabor wavelet, 25th Annual international conf. of the IEEE EMBS, Cancun, Mexico, (2003) 930-933.
  37. H. A. Moghaddam, T. T. Khajoie and A. H. Rouhi, A New Algorithm for Image Indexing and Retrieval Using Wavelet Correlogram, Int. Conf. Image Processing, K.N. Toosi Univ. of Technol., Tehran, Iran, 2 (2003) 497-500.
  38. M. Saadatmand T. and H. A. Moghaddam, Enhanced Wavelet Correlogram Methods for Image Indexing and Retrieval, IEEE Int. Conf. Image Processing, K.N. Toosi Univ. of Technol., Tehran, Iran, (2005) 541-544.
  39. M. Saadatmand T. and H. A. Moghaddam, A Novel Evolutionary Approach for Optimizing Content Based Image Retrieval, IEEE Trans. Systems, Man, and Cybernetics, 37 (1) (2007) 139-153.
  40. L. Birgale, M. Kokare, D. Doye, Color and Texture Features for Content Based Image Retrieval, International Conf. Computer Grafics, Image and Visualisation, Washington, DC, USA, (2006) 146 – 149.
  41. M. Subrahmanyam, A. B. Gonde and R. P. Maheshwari, Color and Texture Features for Image Indexing and Retrieval, IEEE Int. Advance Computing Conf., Patial, India, (2009) 1411-1416.
  42. SubrahmanyamMurala, R. P. Maheshwari, R. Balasubramanian, A Correlogram Algorithm for Image Indexing and Retrieval Using Wavelet and Rotated Wavelet Filters, Int. J. Signal and Imaging Systems Engineering.
  43. R.M. Haralick, K. Shangmugam, I. Dinstein, Textural feature for image classification, IEEE Trans. Syst. Man Cybern. SMC-3 (6) (1973) 610–621.
  44. Coggins J.M. and Jain A.K., "A spatial filtering approach to texture analysis," Pattern Recogninon Letters, no.3, pp.195-203, 1985.
  45. http://wang.ist.psu.edu/.
  46. K. Jarrah, S. Krishnan, L. Guan , "Automatic content-based image retrieval using hierarchical clustering algorithms," in Proc. International Joint Conference on Neural Networks (IJCNN '06), Oct. 2006, Vancouver, BC pp. 3532 - 3537.
  47. Ms. Urvashi Chavan1, Prof. N. M. Shahane2, Content Based Image Retrieval Using Clustering, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 10, October 2014
  48. L. Pengyu, J. Kebin, Z. Peizhen, “An effective method of image retrieval based on modified Fuzzy C-Means clustering scheme”, Proceeding ICSP2006.
Index Terms

Computer Science
Information Sciences

Keywords

Structural Statistical approach Pose Illumination Dimensionality

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