Interval Valued Feature Selection for Classification of Logo Images

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

A model for classification of logo images through a symbolic feature selection is proposed in this paper. The proposed model extracts three global features viz., color, texture, and shape from logo images. These features are then fused to emphasize the superiority of feature level fusion strategy. Due to the existence of large variations across the samples in each class, the samples are clustered and represented in the form of symbolic interval valued data during training. The symbolic feature selection is then adopted to show the efficacy of feature sub-setting in classifying the logo images. During testing, a query logo image is classified as one of the members of three classes with only few discriminable set of features using a suitable symbolic classifier. For experimentation purpose, a huge corpus of 5044 color logo images has been used. The proposed model is validated using suitable validity measures viz., f-measure, precision, recall, accuracy, and time. The results with the comparative analysis show the superiority of the symbolic feature selection method with that of without feature selection in terms of time and average f-measure.

Keywords

Logo image K-Means clustering Symbolic feature selection Symbolic classification 

Notes

Acknowledgement

The second author would like to acknowledge the Department of Science Technology, INDIA, for their financial support through DST-INSPIRE fellowship.

References

  1. 1.
    Hesson, A., Androutsos, D.: Logo and trademark detection in images using color wavelet co-occurrence histograms. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp.1233–1236 (2008)Google Scholar
  2. 2.
    Nourbakhsh, F., Karatzas, D., Valveny, E.: A polar-based logo representation based on topological and colour features. In: Proceedings of the 9th IAPR Workshop on Document Analysis Systems, pp. 341–348. ACM Press (2010)Google Scholar
  3. 3.
    Romberg, S., Pueyo, L.G., Lienhart, R., Zwol, R.V.: Scalable logo recognition in real-world images. In: ACM International Conference on Multimedia Retrieval 2011, ICMR 2011 (2011)Google Scholar
  4. 4.
    Kalantidis, Y., Pueyo, L.G., Trevisiol, M., Zwol, R.V., Avrithis, Y.: Scalable triangulation-based logo recognition. In: ACM International Conference on Multimedia Retrieval, ICMR 2011 (2011)Google Scholar
  5. 5.
    Romberg, S., Lienhart, R.: Bundle min-hashing for logo recognition. In: Proceedings of the 3rd ACM International Conference on Multimedia Retrieval, ICMR 2013, pp. 113–120 (2013)Google Scholar
  6. 6.
    Neumann, J., Samet, H., Soffer, A.: Integration of local and global shape analysis for logo classification. Pattern Recogn. Lett. 23, 1449–1457 (2002)CrossRefMATHGoogle Scholar
  7. 7.
    Sun, S.K., Chen, Z.: Logo recognition by mobile phone cameras. J. Inf. Sci. Eng. 27, 545–559 (2011)MathSciNetGoogle Scholar
  8. 8.
    Arafat, Y.S., Saleem, M., Hussain, A.S.: Comparative analysis of invariant schemes for logo classification. In: International Conference on Emerging Technologies, pp. 256–261 (2009)Google Scholar
  9. 9.
    Kumar, N.V., Kantha, P.V., Govindaraju, K.N., Guru, D.S.: Fusion of features for classification of logos. Procedia Comput. Sci. 85, 370–379 (2016)CrossRefGoogle Scholar
  10. 10.
    Guru, D.S., Kumar, N.V.: Symbolic representation and classification of logos. In: Proceedings of International Conference on Computer Vision and Image Processing (CVIP-2016). AISC Series, vol. 459, pp 555–569. Springer, Singapore (2016)Google Scholar
  11. 11.
    Duda, O.R., Hart, E.P., Stork, G.D.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2000)MATHGoogle Scholar
  12. 12.
    Guru, D.S., Kumar, N.V.: Class specific feature selection for interval valued data through interval K-Means clustering. In: RTIP2R 2016. CCIS, vol. 709, pp. 228–239. Springer, Singapore (2017)Google Scholar
  13. 13.
    Ichino, M.: Feature selection for symbolic data classification. In: New Approaches in Classification and Data Analysis, pp. 423–429. Springer, Heidelberg (1994)Google Scholar
  14. 14.
    Kiranagi, B.B., Guru D.S., Ichino M.: Exploitation of multivalued type proximity for symbolic feature selection. In: Proceedings of the Internal Conference on Computing: Theory and Applications, pp. 320–324. IEEE (2007)Google Scholar
  15. 15.
    Hedjazi, L., Martin, A.J., Lann, M.V.L.: Similarity-margin based feature selection for symbolic interval data. Pattern Recogn. Lett. 32, 578–585 (2011)CrossRefGoogle Scholar
  16. 16.
    Guru, D.S., Kumar, N.V.: Novel feature ranking criteria for interval valued feature selection. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 149–155. IEEE (2016)Google Scholar
  17. 17.
    Guru, D.S., Prakash, H.N.: Online signature verification and recognition: an approach based on symbolic representation. IEEE Trans. Pattern Anal. Mach. Int. 31(6), 1059–1073 (2009)CrossRefGoogle Scholar
  18. 18.
    Silva, A.P.D., Brito, P.: Linear discriminant analysis for interval data. Comput. Stat. 21, 289–308 (2006)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Barros, A.P., Carvalho, F.A.T., Neto, E.A.L.: A pattern classifier for interval-valued data based on multinomial logistic regression model. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 541–546 (2012)Google Scholar
  20. 20.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc., Upper Saddle River (1988)MATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Studies in Computer ScienceUniversity of MysoreMysoreIndia

Personalised recommendations