Integrating salient colors with rotational invariant texture features for image representation in retrieval systems

  • Muhammad Sajjad
  • Amin Ullah
  • Jamil Ahmad
  • Naveed Abbas
  • Seungmin Rho
  • Sung Wook Baik
Article
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Abstract

Content based image retrieval (CBIR) systems allow searching for visually similar images in large collections based on their contents. Visual contents are usually represented based on their properties like colors, shapes, and textures. In this paper, we propose to integrate two properties of images for constructing a discriminative and robust representation. Firstly, the input image is transformed into the HSV color space and then quantized into a limited number of representative colors. Secondly, texture features based on uniform patterns of rotated local binary patterns (RLBP) are extracted. The characteristics of color histogram populated from the quantized images and texture features are compared and analyzed for image representation. Consequently, the quantized color histogram and histogram of uniform patterns in RLBP are fused together to form a feature vector. Experimental evaluations with frequently used datasets show that the proposed method yields better results as compared to other state-of-the-art techniques.

Keywords

Content based image retrieval Visual features Salient colors Texture features 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (No.2016R1A2B4011712).

References

  1. 1.
    Ahmad J. et al. (2015) SSH: Salient structures histogram for content based image retrieval. In Network-Based Information Systems (NBiS), 2015 18th international Conference on. IEEEGoogle Scholar
  2. 2.
    Ahmad J, Sajjad M, Mehmood I et al (2015) Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. J Real-Time Image Proc. https://doi.org/ 10.1007/s11554-015-0536-0
  3. 3.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefMATHGoogle Scholar
  4. 4.
    Bala A, Kaur T (2016) Local texton XOR patterns: a new feature descriptor for content-based image retrieval. Eng Sci Technol Int J 19(1):101–112CrossRefGoogle Scholar
  5. 5.
    Boutell M and Luo J (2004) A generalized temporal context model for semantic scene classification. In Computer Vision and Pattern Recognition Workshop, 2004. CVPRW'04. Conference on. IEEEGoogle Scholar
  6. 6.
    Eimer M (2014) The neural basis of attentional control in visual search. Trends Cogn Sci 18(10):526–535CrossRefGoogle Scholar
  7. 7.
    Hoi SC et al. (2008) Semi-supervised SVM batch mode active learning for image retrieval. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEEGoogle Scholar
  8. 8.
    Duan L-Y, Chen J, Ji R, Huang T, Gao W (2013) Learning compact visual descriptors for low bit rate mobile landmark search. In IJCAI Proceedings-International Joint Conference on Artificial. AI Mag 34(2):67Google Scholar
  9. 9.
    Junling L et al. (2011) Image retrieval based on weighted blocks and color feature. In Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on. IEEEGoogle Scholar
  10. 10.
    Kastner S, Ungerleider LG (2001) The neural basis of biased competition in human visual cortex. Neuropsychologia 39(12):1263–1276CrossRefGoogle Scholar
  11. 11.
    Liu G-H et al (2010) Image retrieval based on multi-texton histogram. Pattern Recogn 43(7):2380–2389CrossRefMATHGoogle Scholar
  12. 12.
    Liu G-H et al (2011) Image retrieval based on micro-structure descriptor. Pattern Recogn 44(9):2123–2133CrossRefGoogle Scholar
  13. 13.
    Livingstone MS, Hubel DH (1984) Anatomy and physiology of a color system in the primate visual cortex. J Neurosci 4(1):309–356Google Scholar
  14. 14.
    Marée R, Geurts P, Wehenkel L (2007) Content-based image retrieval by indexing random subwindows with randomized trees. In Asian Conference on Computer Vision. SpringerGoogle Scholar
  15. 15.
    Mehta R, Egiazarian K (2016) Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recogn Lett 71:16–22CrossRefGoogle Scholar
  16. 16.
    Müller H et al (2001) Performance evaluation in content-based image retrieval: overview and proposals. Pattern Recogn Lett 22(5):593–601CrossRefMATHGoogle Scholar
  17. 17.
    Murala S, Maheshwari R, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886MathSciNetCrossRefGoogle Scholar
  18. 18.
    Rahimi M, Moghaddam ME (2015) A content-based image retrieval system based on color ton distribution descriptors. SIViP 9(3):691–704CrossRefGoogle Scholar
  19. 19.
    Sethi, I.K., I.L. Coman, and D. Stan. (2001) Mining association rules between low-level image features and high-level concepts. In aerospace/defense sensing, simulation, and controls. International Society for Optics and PhotonicsGoogle Scholar
  20. 20.
    Shao H, Svoboda T, Van Gool L (2003) Zubud-zurich buildings database for image based recognition. Computer Vision Lab, Swiss Federal Institute of Technology, Switzerland, Tech. Rep 260:20Google Scholar
  21. 21.
    Smeulders AW et al (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380CrossRefGoogle Scholar
  22. 22.
    Takala V, Ahonen T, Pietikäinen M (2005) Block-based methods for image retrieval using local binary patterns. In Scandinavian Conference on Image Analysis. SpringerGoogle Scholar
  23. 23.
    Tang J, Lewis PH (2007) A study of quality issues for image auto-annotation with the corel dataset. IEEE Trans Circuits Syst Video Technol 17(3):384–389CrossRefGoogle Scholar
  24. 24.
    Vatamanu OA et al. (2013) Content-based image retrieval using local binary pattern, intensity histogram and color coherence vector. In E-health and bioengineering conference (EHB), 2013. IEEEGoogle Scholar
  25. 25.
    Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348CrossRefGoogle Scholar
  26. 26.
    Wang X, Wang Z (2013) A novel method for image retrieval based on structure elements’ descriptor. J Vis Commun Image Represent 24(1):63–74CrossRefGoogle Scholar
  27. 27.
    Won CS, Park DK, Park S-J (2002) Efficient use of MPEG-7 edge histogram descriptor. ETRI J 24(1):23–30MathSciNetCrossRefGoogle Scholar
  28. 28.
    Yu Y-H, Lee T-T, Chen P-Y, Kwok N (2014) On-chip real-time feature extraction using semantic annotations for object recognition. J Real-Time Image Proc 9:1–16Google Scholar
  29. 29.
    Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928CrossRefGoogle Scholar
  30. 30.
    Zhou, J., T. Xu, and W. Gao (2014) Content based image retrieval using local directional pattern and color histogram, In Optimization and Control Techniques and Applications. Springer. p. 197–211Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Muhammad Sajjad
    • 1
  • Amin Ullah
    • 1
    • 2
  • Jamil Ahmad
    • 2
  • Naveed Abbas
    • 1
  • Seungmin Rho
    • 3
  • Sung Wook Baik
    • 2
  1. 1.Digital Image Processing Laboratory, Department of Computer ScienceIslamia CollegePeshawarPakistan
  2. 2.Intelligent Media Laboratory, College of Software and Convergence TechnologySejong UniversitySeoulRepublic of Korea
  3. 3.Department of Media SoftwareSungkyul UniversityAnyangRepublic of Korea

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