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An efficient seed points selection approach in dominant color descriptors (DCD)

  • L. K. PavithraEmail author
  • T. Sree Sharmila
Article
  • 56 Downloads

Abstract

The content-based image retrieval (CBIR) system accepts the input in the form of images and retrieves the relevant images from the database. The CBIR system automatically extracts the prominent key information from the image involved in the retrieval task. The color is one of the key information of the image and it is represented by dominant color descriptors (DCD). Here, similar colors get clustered and the mean value of each cluster represents the dominant color. The random number of unstable cluster formation in DCD alleviates the CBIR system performance. The proposed work has minimized the drawback of DCD by introducing seed points selection based on the mean, maximum and minimum value of the color pixels present in the image. Moreover, this work suggests the optimal cluster number by validating the different combinations of the proposed stable dominant color clusters. The retrieval precision of the proposed CBIR has improved since this work gives equal weight for both the dominant color and its occurrence probability in distance metric calculation. Finally, four standard datasets namely Wang’s, Corel-10k, OT-scene, and Oxford flower are considered for evaluation, and it gives more number of relevant images compared to the state-of-the-art dominant color feature extraction techniques used on these datasets.

Keywords

Dominant color descriptor Image retrieval Initial seed point selection K_Means clustering Similarity measure 

References

  1. 1.
    Jiang, X., Li, C., Sun, J.: A modified k-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-0949-6 Google Scholar
  2. 2.
    Ye, M., Johns, E., Walter, B., Meining, A., Yang, G.Z.: An image retrieval framework for real-time endoscopic image retargeting. Int. J. CARS. 12, 1281 (2017).  https://doi.org/10.1007/s11548-017-1620-7 CrossRefGoogle Scholar
  3. 3.
    Abdullah, S.L.S., Hambali, H.A., Jamil, N.: Segmentation of natural images using an improved thresholding-based technique. Procedia Eng. 41, 938–944 (2012)CrossRefGoogle Scholar
  4. 4.
    Alkhalaf, S., Alfarraj, O., Hemeida, A.M.: Fuzzy-VQ image compression based hybrid PSOGSA optimization algorithm. In: IEEE International Conference on Fuzzy Systems (FUZZIEEE), pp. 1–6 (2015)Google Scholar
  5. 5.
    Equitz, W.H.: A new vector quantization clustering algorithm. IEEE Trans. Acoust. Speech Signal Process. 37(10), 1568–1575 (1989)CrossRefGoogle Scholar
  6. 6.
    EmreCelebi, M.: Improving the performance of k-means for color quantization. Image Vis. Comput. 29(4), 260–271 (2011)CrossRefGoogle Scholar
  7. 7.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press, Berkeley, pp. 281–297 (1967)Google Scholar
  8. 8.
    Bai, C., Zhang, J., Liu, Z., Zhao, W.L.: K-means based histogram using multiresolution feature vectors for color texture database retrieval. Multimed. Tools Appl. 74, 1469–1488 (2015)CrossRefGoogle Scholar
  9. 9.
    Agrawal, S.C., Jalal, A.S., Tripathi, R.K.: A hybrid method for image categorization using shape descriptors and histogram of oriented gradients. In: Proceedings of International Conference on Computer Vision and Image Processing, pp. 285–295 (2017)Google Scholar
  10. 10.
    Han, C.: Improved SLIC imagine segmentation algorithm based on K-means. Clust. Comput. 20, 1017–1023 (2017).  https://doi.org/10.1007/s10586-017-0792-9 CrossRefGoogle Scholar
  11. 11.
    Pei, J., Zhao, L., Dong, X., Dong, X.: Effective algorithm for determining the number of clusters and its application in image segmentation. Clust. Comput. 20, 2845–2854 (2017).  https://doi.org/10.1007/s10586-017-1083-1 CrossRefGoogle Scholar
  12. 12.
    Zhou, Y., Ren, Q.: Fuzzy c-means clustering algorithm for performance improvement of ENN. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1346-x Google Scholar
  13. 13.
    Chen, S.X., Li, F.W., Zhu, W.L., Zhang, T.Q.: Initial codebook algorithm of vector quantization. IEICE Trans. Inf. Syst. E91-D(8), 2189–2191 (2008)CrossRefGoogle Scholar
  14. 14.
    Katsavounidis, I., Kuo, C.C.J., Zhang, Z.: A new initialization technique for generalized Lloyd iteration. IEEE Signal Process. Lett. 1(10), 144–146 (1994)CrossRefGoogle Scholar
  15. 15.
    Lai, J.Z.C., Liaw, Y.C., Liu, J.: A fast VQ codebook generation algorithm using code word displacement. Pattern Recogn. 41(1), 315–319 (2008)CrossRefzbMATHGoogle Scholar
  16. 16.
    Wang, L., Lu, Z.M., Ma, L.H., Feng, Y.P.: VQ codebook design using modified K-means algorithm with feature classification and grouping based initialization. Multimed. Tools Appl. 77–7, 8495–8510 (2018).  https://doi.org/10.1007/s11042-017-4747-1 CrossRefGoogle Scholar
  17. 17.
    Sajjad, M., Ullah, A., Ahmad, J., Abbas, N., Rho, S., Baik, S.W.: Integrating salient colors with rotational invariant texture features for image representation in retrieval systems. Multimed. Tools Appl. 77, 4769–4789 (2018).  https://doi.org/10.1007/s11042-017-5010-5 CrossRefGoogle Scholar
  18. 18.
    Fadaei, S., Amirfattahi, R., Ahmadzadeh, M.R.: New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features. IET Image Process. 11(2), 89–98 (2017)CrossRefGoogle Scholar
  19. 19.
    Yang, N.C., Chang, W.H., Kuo, C.M., Li, T.H.: A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval. J. Vis. Commun. Image Represent. 19, 92–105 (2008).  https://doi.org/10.1016/j.jvcir.2007.05.003 CrossRefGoogle Scholar
  20. 20.
    Pavithra, L.K., Sree Sharmila, T.: Retrieval of homogeneous images using appropriate color space selection. In: International Conference on Computational Intelligence in Data Mining, pp. 739–747 (2017).  https://doi.org/10.1007/978-981-10-3874-7_70
  21. 21.
    Clausi, D.: K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation. Pattern Recogn. Lett. 35(9), 1959–1972 (2002)CrossRefzbMATHGoogle Scholar
  22. 22.
    Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)CrossRefGoogle Scholar
  23. 23.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  24. 24.
    Liu, G.-H., Yang, J.-Y., et al.: Content-based image retrieval using computational visual attention model. Pattern Recogn. 48(8), 2554–2566 (2015)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Nilsback, M.-E., Zisserman, A.: A visual vocabulary for flower classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1447–1454 (2006)Google Scholar
  26. 26.
    Kassambara, A.: Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning (Multivariate Analysis), vol. 1. STHDA, 1st edn (2017)Google Scholar
  27. 27.
    Ma, W.Y., Deng, Y., Manjunath, B.S.: Tools for texture/color based search of images. In: SPIE Conference on Human Vision and Electronic Imaging II, pp. 496–507 (1997)Google Scholar
  28. 28.
    Pavithra, L.K., Sree Sharmila, T.: An efficient framework for image retrieval using color, texture and edge features. Comput. Electr. Eng. (2017).  https://doi.org/10.1016/j.compeleceng.2017.08.030 Google Scholar
  29. 29.
    Mojsilovic, A., Kovacevic, J., Hu, J., Safranek, R.J., Kicha Ganapathy, S.: Matching and retrieval based on the vocabulary and grammar of color patterns. IEEE Trans. Image Process. 9(1), 38–54 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information TechnologySSN College of EngineeringChennaiIndia

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