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Local extreme complete trio pattern for multimedia image retrieval system

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Abstract

This paper presents a new feature descriptor, namely local extreme complete trio pattern (LECTP) for image retrieval application. The LECTP extracts complete extreme to minimal edge information in all possible directions using trio values. The LECTP integrates the local extreme sign trio patterns (LESTP) with magnitude local operator (MLOP) for image retrieval. The performance of the LECTP is tested by conducting three experiments on Corel-5 000, Corel-10 000 and MIT-VisTex color databases, respectively. The results after investigation show a significant improvement in terms of average retrieval precision (ARP) and average retrieval rate (ARR) as compared to the other state-of-the art techniques in content based image retrieval (CBIR).

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References

  1. Y. Rui, T. S. Huang, S. F. Chang. Image retrieval: Current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39–62, 1999.

    Article  Google Scholar 

  2. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349–1380, 2000.

    Article  Google Scholar 

  3. M. Kokare, B. N. Chatterji, P. K. Biswas. A survey on current content based image retrieval methods. IETE Journal of Research, vol. 48, no. 3–4, pp. 261–271, 2002.

    Article  Google Scholar 

  4. Y. Liu, D. S. Zhang, G. J. Lu, W. Y. Ma. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, vol. 40, no. 1, pp. 262–282, 2007.

    Article  MATH  Google Scholar 

  5. M. A. Stricker, M. Oreng. Similarity of color images. In Proceedings of the SPIE, Storage and Retrieval for Image and Video Databases III, SPIE, San Jose, USA, pp. 381–392, 1995.

    Chapter  Google Scholar 

  6. G. Pass, R. Zabih, J. Miller. Comparing images using color coherence vectors. In Proceedings of the 4th ACM International Conference on Multimedia, ACM, Boston, USA, pp. 65–73, 1997.

    Google Scholar 

  7. M. J. Swain, D. H. Ballar. Indexing via color histograms. In Proceedings of the 3rd International Conference on Computer Vision, IEEE, Osaka, Japan, pp. 390–393, 1990.

    Google Scholar 

  8. J. Huang, S. R. Kumar, M. Mitra. Combining supervised learning with color correlograms for content-based image retrieval. In Proceedings of the 5th ACM International Conference on Multimedia, ACM, Seattle, USA, pp. 325–334, 1997.

    Google Scholar 

  9. J. R. Smith, S. F. Chang. Automated binary texture feature sets for image retrieval. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing, IEEE, Atlanta, USA, pp. 2239–2242, 1996.

    Google Scholar 

  10. A. Ahmadian, A. Mostafa. An efficient texture classification algorithm using Gabor wavelet. In Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Cancun, Mexico, pp. 930–933, 2003.

    Google Scholar 

  11. H. A. Moghaddam, T. T. Khajoie, A. H. Rouhi, M. S. Tarzjan. Wavelet Correlogram: A new approach for image indexing and retrieval. Pattern Recognition, vol. 38, no. 12, pp. 2506–2518, 2005.

    Article  Google Scholar 

  12. A. B. Gonde, R. P. Maheshwari, R. Balasubramanian. Content-based image retrieval using colour feature and colour bit planes. International Journal of Signal and Imaging Systems Engineering, vol. 3, no. 2, pp. 105–115, 2010.

    Article  Google Scholar 

  13. M. Kokare, P. K. Biswas, B. N. Chatterji. Texture image retrieval using rotated wavelet filters. Pattern Recognition Letters, vol. 28, no. 10, pp. 1240–1249, 2007.

    Article  Google Scholar 

  14. M. Kokare, P. K. Biswas, B. N. Chatterji. Texture image retrieval using new rotated complex wavelet filters. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 35, no. 6, pp. 1168–1178, 2005.

    Article  Google Scholar 

  15. M. Subrahmanyam, R. P. Maheshwari, R. Balasubramanian. A correlogram algorithm for image indexing and retrieval using wavelet and rotated wavelet filters. International Journal of Signal and Imaging Systems Engineering, vol. 4, no. 1, pp. 27–34 2011.

    Article  Google Scholar 

  16. T. Ojala, M. Pietikäinen, D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.

    Article  Google Scholar 

  17. T. Ojala, M. Pietikainen, T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.

    Article  MATH  Google Scholar 

  18. M. Pietikäinen, T. Ojala, Z. Xu. Rotation-invariant texture classification using feature distributions. Pattern Recognition, vol. 33, no. 1, pp. 43–52, 2000.

    Article  Google Scholar 

  19. Z. H. Guo, D. Zhang, D. Zhang. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1657–1663, 2010.

    Article  MathSciNet  Google Scholar 

  20. M. Subrahmanyam, Q. M. Jonathan, R. P. Maheshwari, R. Balasubramanian. Modified color motif co-occurrence matrix for image indexing and retrieval. Computers and Electrical Engineering, vol. 39, no. 3, pp. 762–774, 2013.

    Article  Google Scholar 

  21. S. Murala, R. P. Maheshwari, R. Balasubramanian. Directional binary wavelet patterns for biomedical image indexing and retrieval. Journal of Medical Systems, vol. 36, no. 5, pp. 2865–2879, 2012.

    Article  MathSciNet  Google Scholar 

  22. S. Murala, R. P. Maheshwari, R. Balasubramanian. Local tetra patterns: A new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2874–2886, 2012.

    Article  MathSciNet  Google Scholar 

  23. M. Subrahmanyam, R. P. Maheshwari, R. Balasubramanian. Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking. Signal Processing, vol. 92, no. 6, pp. 1467–1479, 2012.

    Article  Google Scholar 

  24. M. Subrahmanyam, R. P. Maheshwari, R. Balasubramanian. Directional local extrema patterns: A new descriptor for content based image retrieval. International Journal of Multimedia Information Retrieval, vol. 1, no. 3, pp. 191–203, 2012.

    Article  Google Scholar 

  25. S. Vipparthi, S. Nagar. Directional local ternary patterns for multimedia image indexing and retrieval. International Journal of Signal and Imaging Systems Engineering, to be published.

  26. S. K. Vipparthi, S. K. Nagar. Directional local quinary patterns: A new feature descriptor for image indexing and retrieval. In Proceedings of the International Conference on Advanced Computing, Networking, and Informatics, Raipur, India, pp. 837–844, 2014.

    Google Scholar 

  27. S. K. Vipparthi, S. K. Nagar. Multi-joint histogram based modelling for image indexing and retrieval. Computers and Electrical Engineering, vol. 40, no. 8, pp. 163–173, 2014.

    Article  Google Scholar 

  28. S. K. Vipparthi, S. K. Nagar. Expert image retrieval system using directional local motif XOR patterns. Expert Systems with Applications, vol. 41, no. 17, pp. 8016–8026, 2014.

    Article  Google Scholar 

  29. S. K. Vipparthi, S. K. Nagar. Integration of color and local derivative pattern features for content-based image indexing and retrieval. Journal of the Institution of Engineers (India): Series B, vol. 96, no. 3, pp. 251–263, 2014.

    Article  Google Scholar 

  30. V. Takala, T. Ahonen, M. Pietikainen. Block-based methods for image retrieval using local binary patterns. In Proceedings of the 14th Scandinavian Conference on Image Analysis, Joensuu, Finland, pp. 882–891, 2005.

    Google Scholar 

  31. M. Heikkil, M. Pietikäinen, C. Schmid. Description of interest regions with local binary patterns. Pattern Recognition, vol. 42, no. 3, pp. 425–436, 2009.

    Article  MATH  Google Scholar 

  32. X. Y. Tan, B. Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1635–1650, 2010.

    Article  MathSciNet  Google Scholar 

  33. Y. Koda, I. Kanaya, K. Sato. Modeling real objects for Kansei-based shape retrieval. International Journal of Automation and Computing, vol. 4, no. 1, pp. 14–17, 2007.

    Article  Google Scholar 

  34. G. Sahoo, K. Tapas, B. L. Raina, C. M. Bhatia. Text extraction and enhancement of binary images using cellular automata. International Journal of Automation and Computing, vol. 6, no. 3, pp. 254–260, 2009.

    Article  Google Scholar 

  35. Y. Z. Lu. A novel face recognition algorithm for distinguishing faces with various angles. International Journal of Automation and Computing, vol. 5, no. 2, pp. 193–197, 2008.

    Article  Google Scholar 

  36. Corel Database, [Online], Available: http://wang.ist.psu.edu/docs/related.shtml.

  37. MIT Vision and Modeling Group, Vision Texture, [Online], Available: http://vismod.www.media.mit.edu.

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Authors and Affiliations

  1. Indian Institute of Technology, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh, India

    Santosh Kumar Vipparthi & Shyam Krishna Nagar

  2. Malaviya National Institute of Technology, Jaipur, 302017, Rajastan, India

    Santosh Kumar Vipparthi

Authors
  1. Santosh Kumar Vipparthi
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  2. Shyam Krishna Nagar
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Corresponding author

Correspondence to Santosh Kumar Vipparthi.

Additional information

Recommended by Associate Editor De Xu

Santosh Kumar Vipparthi received the B.Eng. degree in electrical and electronics engineering from Andhra University, India in 2007, the M.Eng. degree in systems engineering from the Indian Institute of Technology, India in 2010, where he is currently a Ph. D. degree candidate at Department of Electrical Engineering. Currently, he is working as an assistant professor at Department of Computer Science and Engineering, Malaviya National Institute of Technology, India.

His research interests include image processing, content-based image retrieval and object tracking.

ORCID iD: 0000-0002-5672-3537

Shyam Krishna Nagar received the Ph.D. degree from Department of Electrical Engineering, Indian Institute of Technology Roorkee, India in 1991. He is currently working as a professor at Department of Electrical Engineering, Indian Institute of Technology, Banaras Hindu University, India.

His research interests include image processing, content-based image retrieval, digital control systems and model order reduction.

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Vipparthi, S.K., Nagar, S.K. Local extreme complete trio pattern for multimedia image retrieval system. Int. J. Autom. Comput. 13, 457–467 (2016). https://doi.org/10.1007/s11633-016-0978-2

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  • DOI: https://doi.org/10.1007/s11633-016-0978-2

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