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Journal of Engineering
Volume 2014, Article ID 439218, 13 pages
http://dx.doi.org/10.1155/2014/439218
Research Article

A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

1Pimpri Chinchwad College of Engineering, Akurdi, Sector 26, Pradhikaran, Nigdi, Pune, Maharashtra 411033, India
2Xavier Institute of Social Service, Dr. Camil Bulcke Path (Purulia Road), P.O. Box 7, Ranchi, Jharkhand 834001, India
3A.K. Choudhury School of Information Technology, University of Calcutta, 92 APC Road, Kolkata, West Bengal 700009, India

Received 8 July 2014; Revised 20 October 2014; Accepted 21 October 2014; Published 18 November 2014

Academic Editor: Jie Zhou

Copyright © 2014 Sudeep Thepade et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Andreopoulos and J. K. Tsotsos, “50 Years of object recognition: directions forward,” Computer Vision and Image Understanding, vol. 117, no. 8, pp. 827–891, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Thepade, R. Das, and S. Ghosh, “Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers,” in Proceedings of the Annual IEEE India Conference (INDICON '13), pp. 1–6, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. H. B. Kekre, S. Thepade, R. K. Das, and S. Ghosh, “Multilevel block truncation coding with diverse color spaces for image classification,” in Proceedings of the International Conference on Advances in Technology and Engineering (ICATE '13), pp. 1–7, January 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Niblack, An Introduction to Digital Image Processing, Prentice Hall, Englewood Cliffs, NJ, USA, 1986.
  5. J. Bernsen, “Dynamic thresholding of gray level images,” in Proceedings of the International Conference on Pattern Recognition (ICPR '86), pp. 1251–1255, 1986.
  6. J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,” Pattern Recognition, vol. 33, no. 2, pp. 225–236, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. N. Otsu, “A threshold selection method from gray-level histogram,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Valizadeh, N. Armanfard, M. Komeili, and E. Kabir, “A novel hybrid algorithm for binarization of badly illuminated document images,” in Proceedings of the 14th International CSI Computer Conference (CSICC '09), pp. 121–126, Tehran, Iran, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. Y.-F. Chang, Y.-T. Pai, and S.-J. Ruan, “An efficient thresholding algorithm for degraded document images based on intelligent block detection,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '08), pp. 667–672, October 2009. View at Scopus
  10. B. Gatos, I. Pratikakis, and S. J. Perantonis, “Efficient binarization of historical and degraded document images,” in Proceedings of the 8th IAPR International Workshop on Document Analysis Systems (DAS '08), pp. 447–454, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. M. E. Elalami, “A novel image retrieval model based on the most relevant features,” Knowledge-Based Systems, vol. 24, no. 1, pp. 23–32, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. P. S. Hiremath and J. Pujari, “Content based image retrieval using color, texture and shape features,” in Proceedings of the 15th International Conference on Advanced Computing and Communication (ADCOM '07), pp. 780–784, December 2007. View at Scopus
  13. M. Banerjee, M. K. Kundu, and P. Maji, “Content-based image retrieval using visually significant point features,” Fuzzy Sets and Systems, vol. 160, no. 23, pp. 3323–3341, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. H. A. Jalab, “Image retrieval system based on color layout descriptor and Gabor filters,” in Proceedings of the IEEE Conference on Open Systems (ICOS '11), pp. 32–36, IEEE, Langkawi, Malaysia, September 2011. View at Publisher · View at Google Scholar
  15. G.-L. Shen and X.-J. Wu, “Content based image retrieval by combining color texture and CENTRIST,” in Proceedings of the Constantinides International Workshop on Signal Processing (CIWSP '13), pp. 1–4, January 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Irtaza, M. A. Jaffar, E. Aleisa, and T. S. Choi, “Embedding neural networks for semantic association in content based image retrieval,” Multimedia Tools and Applications, pp. 1–21, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Rahimi and M. E. Moghaddam, “A content-based image retrieval system based on color ton distribution descriptors,” Signal, Image and Video Processing, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Li and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075–1088, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Sridhar, “Image features representation and description,” in Digital Image Processing, pp. 483–486, India Oxford University Press, New Delhi, India, 2011. View at Google Scholar
  20. L. Xu, A. Krzyzak, and C. Y. Suen, “Methods of combining multiple classifiers and their applications to handwriting recognition,” IEEE Transactions on Systems, Man and Cybernetics, vol. 22, no. 3, pp. 418–435, 1992. View at Publisher · View at Google Scholar · View at Scopus
  21. S. B. Kotsiantis, “Supervised machine learning: a review of classification techniques,” Informatica, vol. 31, no. 3, pp. 249–268, 2007. View at Google Scholar · View at MathSciNet · View at Scopus
  22. A. J. Bishara and J. B. Hittner, “Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches,” Psychological Methods, vol. 17, no. 3, pp. 399–417, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. O. T. Yıldız, Ö. Aslan, and E. Alpaydın, “Multivariate statistical tests for comparing classification algorithms,” in Learning and Intelligent Optimization, vol. 6683 of Lecture Notes in Computer Science, pp. 1–15, Springer, Berlin, Germany, 2011. View at Publisher · View at Google Scholar
  24. J. K. Sharma, Fundamentals of Business Statistics, Vikash Publishing House, 2nd edition, 2014.