A Feature Encoding Based on Low Space Complexity Codebook Called Fuzzy Codebook for Image Recognition


For image recognition, a codebook approach is generally used for representing an image to a feature vector. In this approach, the codebook, which is visual vocabulary, is generated for each local feature framework in the case of using multiple local feature frameworks. Hence, the codebook is required that is a small memory footprint. Image representation based on a compact codebook by using fuzzy clustering has been presented, but it includes a high computational complexity operation. This paper presents a reducing computational complexity in the image representation step and experimental results of online classifier.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Berg, T., Liu, J., Lee, S.W., Alexander, M.L., Jacobs, D.W., Belhumeur, P.N.: Birdsnap: large-scale fine-grained visual categorization of birds. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23–28, 2014, pp. 2019–2026. IEEE (2014). https://doi.org/10.1109/CVPR.2014.259

  2. 2.

    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  3. 3.

    Nakayama, H.: Nlab-utokyo at imageclef 2013 plant identification task. In: Working Notes for CLEF 2013 Conference , Valencia, Spain, Sept 23–26, 2013 (2013)

  4. 4.

    Delhumeau, J., Gosselin, P.H., Jégou, H., Pérez, P.: Revisiting the vlad image representation. In: Proceedings of the 21st ACM International Conference on Multimedia, MM ’13, pp. 653–656. ACM, New York (2013). https://doi.org/10.1145/2502081.2502171

  5. 5.

    Nishiyama, M., Okabe, T., Sato, I., Sato, Y.: Aesthetic quality classification of photographs based on color harmony. In: CVPR, pp. 33–40. IEEE Computer Society (2011)

  6. 6.

    Yanai, K., Maruyama, T.: A cooking recipe recommendation system with visual recognition of food ingredients. iJIM 8, 28–34 (2014)

    Google Scholar 

  7. 7.

    Dhar, S., Ordonez, V., Berg, T.L.: High level describable attributes for predicting aesthetics and interestingness. In: CVPR, pp. 1657–1664. IEEE Computer Society (2011)

  8. 8.

    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  9. 9.

    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition 2007 (CVPR’07) (2007)

  10. 10.

    Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: European Conference on Computer Vision. Springer, Berlin (2006)

  11. 11.

    Perronnin, F., Dance, C.R.: Fisher kernels on visual vocabularies for image categorization. In: CVPR. IEEE Computer Society (2007)

  12. 12.

    Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Proceedings of the 11th European Conference on Computer Vision: Part IV, ECCV’10, pp. 143–156. Springer, Berlin (2010)

  13. 13.

    Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear cnns for fine-grained visual recognition. In: Transactions of Pattern Analysis and Machine Intelligence (PAMI) (2017)

  14. 14.

    Li, P., Wang, Q., Zeng, H., Zhang, L.: Local log-euclidean multivariate gaussian descriptor and its application to image classification. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 803–817 (2017)

    Article  Google Scholar 

  15. 15.

    Liu, L., Wang, P., Shen, C., Wang, L., van den Hengel, A., Wang, C., Shen, H.T.: Compositional model based fisher vector coding for image classification. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2335–2348 (2017). https://doi.org/10.1109/TPAMI.2017.2651061

    Article  Google Scholar 

  16. 16.

    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer, Norwell (1981)

    Google Scholar 

  17. 17.

    Sujatha, K., Keerthana, P., Priya, S.S., Kaavya, E., Vinod, B.: Fuzzy based multiple dictionary bag of words for image classification. Procedia Eng. 38, 2196–2206 (2012)

    Article  Google Scholar 

  18. 18.

    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007). https://doi.org/10.1016/j.cviu.2005.09.012

    Article  Google Scholar 

  19. 19.

    Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)

    MathSciNet  MATH  Google Scholar 

  20. 20.

    Seidenari, L., Serra, G., Bagdanov, A.D., Bimbo, A.D.: Local pyramidal descriptors for image recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 1033–1040 (2014). https://doi.org/10.1109/TPAMI.2013.232

    Article  Google Scholar 

Download references


This work was supported by JSPS KAKENHI Grant Number 25330240.

Author information



Corresponding author

Correspondence to Yukinobu Hoshino.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shinomiya, Y., Hoshino, Y. A Feature Encoding Based on Low Space Complexity Codebook Called Fuzzy Codebook for Image Recognition. Int. J. Fuzzy Syst. 21, 274–280 (2019). https://doi.org/10.1007/s40815-018-0568-2

Download citation


  • Image recognition
  • Fuzzy clustering
  • Feature encoding
  • Fuzzy codebook