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Content-Based Image Retrieval Using Multi-Resolution Multi-Direction Filtering-Based CLBP Texture Features and Color Autocorrelogram Features

  • Bu, Hee-Hyung (School of Computer Science and Engineering, Kyungpook National University) ;
  • Kim, Nam-Chul (School of Electronic Engineering, Kyungpook National University) ;
  • Yun, Byoung-Ju (School of Electronic Engineering, Kyungpook National University) ;
  • Kim, Sung-Ho (School of Computer Science and Engineering, Kyungpook National University)
  • Received : 2018.10.02
  • Accepted : 2019.03.08
  • Published : 2020.08.31

Abstract

We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.

Keywords

Acknowledgement

This study was supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (No. 21A20131600005).

References

  1. Z. Tang, M. Ling, H. Yao, Z. Qian, X. Zhang, J. Zhang, and S. Xu, "Robust image hashing via random Gabor filtering and DWT," Computers, Materials and Continua, vol. 55, no. 2, pp. 331-344, 2018.
  2. L. Chen, H. C. Chen, Z. Li, and Y. Wu, "A fusion approach based on infrared finger vein transmitting model by using multi-light-intensity imaging," Human-centric Computing and Information Sciences, vol. 7, article no. 35, 2017.
  3. S. Akbarov and M. Mehdiyev, "The interface stress field in the elastic system consisting of the hollow cylinder and surrounding elastic medium under 3D non-axisymmetric forced vibration," CMC-Computers, Materials & Continua, vol. 54, no. 1, pp. 61-81, 2018.
  4. E. Hadjidemetriou, M. D. Grossberg, and S. K. Nayar, "Multiresolution histograms and their use for texture classification," in Proceedings of the 3rd International Workshop on Texture Analysis and Synthesis, Nice, France, 2003.
  5. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih, "Image indexing using color correlograms," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 1997, pp. 762-768.
  6. J. Han and K. K. Ma, "Rotation-invariant and scale-invariant Gabor features for texture image retrieval," Image and Vision Computing, vol. 25, no. 9, pp. 1474-1481, 2007. https://doi.org/10.1016/j.imavis.2006.12.015
  7. Y. D. Chun, N. C. Kim, and I. H. Jang, "Content-based image retrieval using multiresolution color and texture features," IEEE Transactions on Multimedia, vol. 10, no. 6, pp. 1073-1084, 2008. https://doi.org/10.1109/TMM.2008.2001357
  8. M. H. Rahman, M. R. Pickering, M. R. Frater, and D. Kerr, "Texture feature extraction method for scale and rotation invariant image retrieval," Electronics Letters, vol. 48, no. 11, pp. 626-627, 2012. https://doi.org/10.1049/el.2012.0507
  9. R. D. Gupta, J. K. Dash, and M. Sudipta, "Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis," Pattern Recognition, vol. 46, no. 12, pp. 3256-3267, 2013. https://doi.org/10.1016/j.patcog.2013.05.026
  10. C. Li, G. Duan, and F. Zhong, "Rotation invariant texture retrieval considering the scale dependence of Gabor wavelet," IEEE Transactions on Image Processing, vol. 24, no. 8, pp. 2344-2354, 2015. https://doi.org/10.1109/TIP.2015.2422575
  11. H. H. Bu, N. C. Kim, C. J. Moon, and J. H. Kim, "Content-based image retrieval using combined color and texture features extracted by multi-resolution multi-direction filtering," Journal of Information Processing Systems, vol. 13, no. 3, pp. 464-475, 2017. https://doi.org/10.3745/JIPS.02.0060
  12. S. R. Dubey, S. K. Singh, and R. K. Singh, "Multichannel decoded local binary patterns for content-based image retrieval," IEEE Transactions on Image Processing, vol. 25, no. 9, pp. 4018-4032, 2016. https://doi.org/10.1109/TIP.2016.2577887
  13. Z. Guo, L. Zhang, and 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. https://doi.org/10.1109/TIP.2010.2044957
  14. T. Ojala, M. Pietikainen, and 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. https://doi.org/10.1109/TPAMI.2002.1017623
  15. H. H. Bu, N. C. Kim, K. W. Park, and S. H. Kim, "Content-based image retrieval using combined texture and color features based on multi-resolution multi-direction filtering and color autocorrelogram," Journal of Ambient Intelligence and Humanized Computing, 2019. https://doi.org/10.1007/s12652-019-01466-0.
  16. Y. D. Chun, S. Y. Seo, and N. C. Kim, "Image retrieval using BDIP and BVLC moments," IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 9, pp. 951-957, 2003. https://doi.org/10.1109/TCSVT.2003.816507
  17. R. Pickard, C. Graszyk, S. Mann, J. Wachman, L. Pickard, and L. Campbell, "Vision Texture (VisTex) database," 1995 [Online]. Available: https://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html.
  18. W. Y. Ma and B. S. Manjunath, "A comparison of wavelet transform features for texture image annotation," in Proceedings of International Conference on Image Processing, Washington, DC, 1995, pp. 256-259.
  19. D. Comaniciu, P. Meer, K. Xu, and D. Tyler, "Retrieval performance improvement through low rank corrections," in Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL), Fort Collins, CO, 1999, pp. 50-54.