A learning automata framework based on relevance feedback for content-based image retrieval

  • Mohsen FathianEmail author
  • Fardin Akhlaghian Tab
  • Karim Moradi
  • Soudeh Saien
Original Article


The need for efficient image browsing and searching motivates the use of Content-Based Image Retrieval (CBIR) systems. However, they suffer from a big gap between high-level image semantics and low-level features. So, a learning process to reduce the gap seems quite useful. This paper presents a novel Learning Automata (LA)-based approach to improve the CBIR systems. Distributed Learning Automata (DLA) is used in this work to learn the relevant images from textual query feedbacks of the users. Subsequently, the retrieved images are ranked according to the learning outcome and similarity measure. In this study, the similarity between images is evaluated based on two color descriptors: the global color histogram and local color auto-correlogram. A thorough observation and comparison of these color descriptors performances are performed with different color spaces and also with various similarity measures. Experimental results on two publicly available databases demonstrate that the performance of the proposed CBIR system after each round is improved and the system could retrieve images compatible with the users’ perception.


Learning automata Users feedback Content-based image retrieval Color feature 


  1. 1.
    Kundu M, Chowdhury M, Banerjee M (2012) Interactive image retrieval using M-band wavelet, earth mover’s distance and fuzzy relevance feedback. Int J Mach Learn Cyber 3(4):285–296. doi: 10.1007/s13042-011-0062-8 CrossRefGoogle Scholar
  2. 2.
    Fakheri M, Sedghi T, Shayesteh MG, Amirani MC (2013) Framework for image retrieval using machine learning and statistical similarity matching techniques. Image Process IET 7(1):1–11. doi: 10.1049/iet-ipr.2012.0104 CrossRefGoogle Scholar
  3. 3.
    Li W, Duan L, Xu D, Tsang IW (2011) Text-based image retrieval using progressive multi-instance learning. In: IEEE International Conference on Computer Vision (ICCV), pp 2049–2055. doi: 10.1109/ICCV.2011.6126478
  4. 4.
    Kato T (1992) Database architecture for content-based image retrieval. In: Image storage and retrieval systems, San Jose, pp 112–123. doi: 10.1117/12.58497
  5. 5.
    Wu Y, Tian Q, Huang TS (2000) Discriminant-EM algorithm with application to image retrieval. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR) 1:222–227. doi: 10.1109/CVPR.2000.855823 Google Scholar
  6. 6.
    Laaksonen J, Koskela M, Laakso S, Oja E (2000) PicSOM—content-based image retrieval with self-organizing maps. Pattern Recognit Lett 21(13–14):1199–1207. doi: 10.1016/S0167-8655(00)00082-9 CrossRefzbMATHGoogle Scholar
  7. 7.
    Ng WY, Lv Y, Zeng Z, Yeung D, Chan PK (2015) Sequential conditional entropy maximization semi-supervised hashing for semantic image retrieval. Int J Mach Learn Cyber. doi: 10.1007/s13042-015-0350-9 Google Scholar
  8. 8.
    En C, Jing F, Lei Z (2009) A unified relevance feedback framework for web image retrieval. IEEE Trans Image Process 18(6):1350–1357. doi: 10.1109/TIP.2009.2017128 MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Yi Y, Feiping N, Dong X, Jiebo L, Yueting Z, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34(4):723–742. doi: 10.1109/TPAMI.2011.170 CrossRefGoogle Scholar
  10. 10.
    Lin K, Yang H, Hsiao J, Chen C (2015) Deep learning of binary hash codes for fast image retrieval. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, pp. 27–35. doi: 10.1109/CVPRW.2015.7301269
  11. 11.
    Alippi C, Polycarpou M, Panayiotou C, Ellinas G (2009) Relevance feedback for content-based image retrieval using support vector machines and feature selection. Lecture notes in computer science. Springer, Berlin, pp 942–951. doi: 10.1007/978-3-642-04274-4_97 Google Scholar
  12. 12.
    Zhang L, Lin F, Zhang B (2001) Support vector machine learning for image retrieval. Proc Int Conf Image Process 2:721–724. doi: 10.1109/ICIP.2001.958595 Google Scholar
  13. 13.
    Kundu MK, Chowdhury M, Rota Bulò S (2015) A graph-based relevance feedback mechanism in content-based image retrieval. Knowl Based Syst 73:254–264. doi: 10.1016/j.knosys.2014.10.009 CrossRefGoogle Scholar
  14. 14.
    Feng L, Wu J, Liu S, Zhang H (2015) Global correlation descriptor: a novel image representation for image retrieval. J Vis Commun Image R 33:104–114. doi: 10.1016/j.jvcir.2015.09.002 CrossRefGoogle Scholar
  15. 15.
    Khemchandani R, Pal A (2016) Tree based multi-category Laplacian TWSVM for content based image retrieval. Int J Mach Learn Cyber. doi: 10.1007/s13042-016-0493-3 Google Scholar
  16. 16.
    Li Jing, Allinson NM (2013) Relevance feedback in content-based image retrieval: a survey. Handb Neural Inf Process 49:433–469. doi: 10.1007/978-3-642-36657-4_13 CrossRefGoogle Scholar
  17. 17.
    Mollakhalili Meybodi M, Meybodi M (2014) Extended distributed learning automata. Appl Intell 41 (3):923–940. doi: 10.1007/s10489-014-0577-2 CrossRefGoogle Scholar
  18. 18.
    Thathachar M, Sastry PS (2002) Varieties of learning automata: an overview. IEEE Trans Syst Man Cybern B Cybern 32(6):711–722. doi: 10.1109/TSMCB.2002.1049606 CrossRefGoogle Scholar
  19. 19.
    Yarahmadi T, Torkestani JA, Zandevakili F (2012) A new method based on distributed learning automata for page ranking in web. Int J Phys Sci 7(13):2066–2075. doi: 10.5897/IJPS11.1708 Google Scholar
  20. 20.
    Khomami MMD, Rezvanian A, Meybodi MR (2016) Distributed learning automata-based algorithm for community detection in complex networks. Int J Mod Phys B. doi: 10.1142/S0217979216500429 MathSciNetGoogle Scholar
  21. 21.
    Guldogan E, Gabbouj M (2008) Feature selection for content-based image retrieval. SIViP 2(3):241–250. doi: 10.1007/s11760-007-0049-9 CrossRefzbMATHGoogle Scholar
  22. 22.
    Sun J, Zhang X, Cui J, Zhou L (2006) Image retrieval based on color distribution entropy. Pattern Recognit Lett. 27(10):1122–1126. doi: 10.1016/j.patrec.2005.12.014 CrossRefGoogle Scholar
  23. 23.
    Fathian M, Akhlaghian Tab F (2011) A novel content-based image retrieval approach using fuzzy combination of color and texture. In: Deng H, Miao D, Lei J, Wang F (eds) Artificial intelligence and computational intelligence, vol 7004. Lecture notes in computer science. Springer, Berlin, pp 12–23. doi: 10.1007/978-3-642-23896-3_2 CrossRefGoogle Scholar
  24. 24.
    Rahimi M, Ebrahimi Moghaddam M (2015) A content-based image retrieval system based on Color Ton Distribution descriptors. SIViP 9(3):691–704. doi: 10.1007/s11760-013-0506-6 CrossRefGoogle Scholar
  25. 25.
    Lu T-C, Chang C-C (2007) Color image retrieval technique based on color features and image bitmap. Inf Process Manag. 43(2):461–472. doi: 10.1016/j.ipm.2006.07.014 MathSciNetCrossRefGoogle Scholar
  26. 26.
    Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715. doi: 10.1109/76.927424 CrossRefGoogle Scholar
  27. 27.
    Aptoula E, Lefevre S (2009) Morphological description of color images for content-based image retrieval. IEEE Trans Image Process 18(11):2505–2517. doi: 10.1109/TIP.2009.2027363 MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Plataniotis KN, Venetsanopoulos AN (2000) Color image processing and applications. Springer, New York. doi: 10.1007/978-3-662-04186-4 CrossRefGoogle Scholar
  29. 29.
    Ogle VE, Stonebraker M (1995) Chabot: retrieval from a relational database of images. Computer 28(9):40–48. doi: 10.1109/2.410150 CrossRefGoogle Scholar
  30. 30.
    Swain M, Ballard D (1991) Color indexing. Int J Comput Vis 7(1):11–32. doi: 10.1007/BF00130487 CrossRefGoogle Scholar
  31. 31.
    Suhasini PS, Sri Rama Krishna K., Murali Krishna, I.V. (2016) Content based image retrieval based on different global and local color histogram methods: a survey. J Inst Eng India Ser B. doi: 10.1007/s40031-016-0223-y Google Scholar
  32. 32.
    Jing H, Kumar SR, Mitra M, Wei-Jing Z, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 762–768. doi: 10.1109/CVPR.1997.609412
  33. 33.
    Huang J, Kumar SR, Mitra M, Zhu W, Zabih R (1999) Spatial color indexing and applications. Int J Comput Vis 35(3):245–268. doi: 10.1023/a:1008108327226 CrossRefGoogle Scholar
  34. 34.
    Thathachar MAL, Harita BR (1987) Learning automata with changing number of actions. IEEE Trans Syst Man Cybern Syst 17(6):1095–1100. doi: 10.1109/TSMC.1987.6499323 CrossRefGoogle Scholar
  35. 35.
    Beigy H, Meybodi MR (2006) Utilizing distributed learning automata to solve stochastic shortest path problems. Int J Uncertain Fuzz 14(05):591–615. doi: 10.1142/S0218488506004217 MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Deza M, Deza E (2009) Encyclopedia of distances. Springer, Berlin, pp 1–583. doi: 10.1007/978-3-64200234-2_1 CrossRefzbMATHGoogle Scholar
  37. 37.
    Emran SM, Ye N (2002) Robustness of Chi-square and Canberra distance metrics for computer intrusion detection. Qual Reliab Eng Int. 18(1):19–28. doi: 10.1002/qre.441 CrossRefGoogle Scholar
  38. 38.
    Fathian M, Akhlaghian Tab F (2011) Content-based image retrieval using color features of partitioned images. In: International Conference on Graphic and Image Processing, pp 82850Q–82850Q-8. doi: 10.1117/12.913302
  39. 39.
    Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: semantics sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23:947–963CrossRefGoogle Scholar
  40. 40.
    Li F-F, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611. doi: 10.1109/TPAMI.2006.79 CrossRefGoogle Scholar
  41. 41.
    Moghaddam HA, Khajoie TT, Rouhi AH, Tarzjan MS (2005) Wavelet correlogram: a new approach for image indexing and retrieval. Pattern Recogn 38(12):2506–2518. doi: 10.1016/j.patcog.2005.05.010 CrossRefGoogle Scholar
  42. 42.
    Junwei H, Ngan KN, Mingjing L, Hong-Jiang Z (2005) A memory learning framework for effective image retrieval. IEEE Trans Image Process 14(4):511–524. doi: 10.1109/TIP.2004.841205 CrossRefGoogle Scholar
  43. 43.
    Yong R, Huang TS, Ortega M, Mehrotra S (1998) Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol 8(5):644–655. doi: 10.1109/76.718510 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Mohsen Fathian
    • 1
    Email author
  • Fardin Akhlaghian Tab
    • 2
  • Karim Moradi
    • 3
  • Soudeh Saien
    • 4
  1. 1.Department of Computer Engineering and Information TechnologyHamedan University of TechnologyHamedanIran
  2. 2.Department of Computer EngineeringUniversity of KurdistanSanandajIran
  3. 3.Computer and Information Technology Engineering DepartmentAmirkabir University of TechnologyTehranIran
  4. 4.Department of Computer EngineeringBu-Ali Sina UniversityHamedanIran

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