Multimedia Tools and Applications

, Volume 61, Issue 2, pp 263–279 | Cite as

Extracting discriminative features for CBIR

  • Zhiping ShiEmail author
  • Xi Liu
  • Qingyong Li
  • Qing He
  • Zhongzhi Shi


Developing low-dimensional discriminative features is crucial for content-based image retrieval (CBIR). In this paper, we present a square symmetrical local binary pattern (SSLBP) texture descriptor, which is a compact symmetrical-invariant variation of local binary pattern (LBP), then we propose a merging 2-class linear discriminant analysis (M2CLDA) method to capture low-dimensional optimal discriminative features in the projection space. M2CLDA calculates discriminant vectors with respect to each class in the one-vs.-all classification scenario and then merges all the discriminant vectors to form a projection matrix. The dimensionality of the M2CLDA space fits in with the number of classes involved. Our experiments show that the SSLBP feature is an effective variation of LBP, and the M2CLDA approach improves the performance of image retrieval and image classification observably as compared with the existed LDA approaches and takes less computation complexity than the kernel discriminant analysis (KDA) methods.


2-class LDA Image retrieval Image classification Discriminative features LBP 


  1. 1.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefGoogle Scholar
  2. 2.
    Belhumeur N, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  3. 3.
    Boujemaa N, Nastar C (1999) Content-based image retrieval at the IMEDIA group of the INRIA 10th DELOS Workshop Audio-Visual Digital Libraries Santorini, Greece, JuneGoogle Scholar
  4. 4.
    Brodatz P (1966) Textures: a photographic album for artists and designers. Dover Publications, New YorkGoogle Scholar
  5. 5.
    Ekenel HK, Stiefelhagen R (2007) IEEE 15th signal processing and communications applications. Anadolu University, Eskişehir, pp 1–4CrossRefGoogle Scholar
  6. 6.
    Fisher RA (1938) The statistical utilization of multiple measurements. Ann Eugenics 8:376–386CrossRefGoogle Scholar
  7. 7.
    Flickner M, Sawhney H, Niblack W et al (1995) Query by image and video content: the QBIC system. IEEE Comput 28(9):23–32CrossRefGoogle Scholar
  8. 8.
    Fukunaga K (1990) Introduction to statistical pattern recognition. Academic, New YorkzbMATHGoogle Scholar
  9. 9.
    Gao D, Vasconcelos N (2004) Discriminant saliency for visual recognition from cluttered scenes. In Proceedings of Neural Information Processing Systems (NIPS), Vancouver, Canada. pages 481–488, 2004Google Scholar
  10. 10.
    Guo ZH, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663MathSciNetCrossRefGoogle Scholar
  11. 11.
    Heikkilä M, Pietikäinen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28(4):657–662CrossRefGoogle Scholar
  12. 12.
    Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recogn 42(3):425–436zbMATHCrossRefGoogle Scholar
  13. 13.
    Jing F, Li M, Zhang H-J, Zhang B (2005) A unified framework for image retrieval using keyword and visual features. IEEE Trans Image Process 14(7):979–989CrossRefGoogle Scholar
  14. 14.
    Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Comm Appl 2(1):1–19CrossRefGoogle Scholar
  15. 15.
    Li Z, Shi Z, Liu X, Li Z, Shi Z (2010) Fusing semantic aspects for image annotation and retrieval. J Vis Comm Image Represent 21:798–805CrossRefGoogle Scholar
  16. 16.
    Li J, Wang JZ (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell 25(9):1075–1088CrossRefGoogle Scholar
  17. 17.
    Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118CrossRefGoogle Scholar
  18. 18.
    Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using LDA-based algorithms. IEEE Trans Neural Network 14(1):195–200CrossRefGoogle Scholar
  19. 19.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29:51–59CrossRefGoogle Scholar
  20. 20.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  21. 21.
    Pietikäinen M, Nurmela T, Mäenpää T, Turtinen M (2004) View-based recognition of real-world textures. Pattern Recogn 37(2):313–323zbMATHCrossRefGoogle Scholar
  22. 22.
    Rui Y, Huang TS (2000) Optimizing learning in image retrieval. Proc. of IEEE Int. Conf. On Computer Vision and Pattern Recognition, Hilton Head, SC, 236–243Google Scholar
  23. 23.
    Shi ZP, Hu H, Li QY, Shi ZZ, Duan CL (2005) Texture spectrum descriptor based image retrieval. J Software 16(6):1039–1045, in ChinesezbMATHCrossRefGoogle Scholar
  24. 24.
    Smith JR, Chang SF (1997) Visually searching the web for content. IEEE Multimed 4(3):12–20CrossRefGoogle Scholar
  25. 25.
    Tucker A (1995) Applied combinatorics, 3rd edn. Wiley, New YorkzbMATHGoogle Scholar
  26. 26.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  27. 27.
    Xi Liu, Zhiping Shi, Zhixin Li, Xishun Wang, Zhongzhi Shi (2010) Sorted label classifier chains for learning images with multi-label. ACM MM’10, October 25–29, Firenze, ItalyGoogle Scholar
  28. 28.
    Yang J, Frangi AF, Yang J-Y, Zhang D, Jin Z (2005) Complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–243CrossRefGoogle Scholar
  29. 29.
    You D, Hamsici OC, Martinez AM (2010) Kernel optimization in discriminant analysis. IEEE Trans Pattern Anal Mach Intell, 01 Sept. 2010.
  30. 30.
    Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recogn 34(10):2067–2070zbMATHCrossRefGoogle Scholar
  31. 31.
    Zhao G, Pietikäinen 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
  32. 32.
    Zhiping Shi, Fei Ye, Qing He, Zhongzhi Shi (2008) Symmetrical invariant LBP Texture descriptor and application for image retrieval. IEEE Congress on Image and Signal Processing. Sanya, China. pages 825–829. May 27–30Google Scholar
  33. 33.
    Zhiping Shi, Qing He, Zhongzhi Shi. (2009) An index and retrieval framework integrating perceptive features and semantics for multimedia databases. Multimedia Tools and Applications, Springer Heidelberg, 42(2):207–231Google Scholar
  34. 34.
    Zhiping Shi, Xi Liu, Qing He, Zhongzhi Shi (2009) Image features optimizing for content-based image retrieval. 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. Pages 260–264Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Zhiping Shi
    • 1
    Email author
  • Xi Liu
    • 2
  • Qingyong Li
    • 3
  • Qing He
    • 2
  • Zhongzhi Shi
    • 2
  1. 1.Beijing Engineering Research Center of High Reliable Embedded System, College of Information EngineeringCapital Normal UniversityBeijingChina
  2. 2.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  3. 3.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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