Content-Based Classification of Images Using Centroid Neural Network with Divergence Measure

  • Dong-Chul Park
  • Chung Nguyen Tran
  • Yunsik Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


The automatic classification of images is an effective way to organize a large-scale image database storing thousands of image files. In this paper, an automatic content-based image classification model using Centroid Neural Networks (CNN) with a Divergence Measure called Divergence-based Centroid Neural Network (DCNN) is proposed. The DCNN algorithm, which employs the divergence measure as its distance measure, is used for clustering of Gaussian Probability Distribution Function (GPDF) data. In comparison with other conventional algorithms, the DCNN designed for probability data has the robustness advantages of utilizing a localized image representation method in which each image is represented by a Gaussian distribution feature vector. Experiments and results show that the proposed classification model yields accuracy improvements of 5.77% and 6.97% over models employing the conventional Divergence-based k-means (Dk-means) and Divergence-based Self Organizing Map (DSOM) algorithms, respectively.


Feature Vector Discrete Cosine Transform Code Vector Winner Neuron Weight Update 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Zhang, H.J.: Image Classification for Content-Based Indexing. IEEE Trans. on Image Processing 10(1), 117–130 (2001)MATHCrossRefGoogle Scholar
  2. 2.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at The End of The Early Years. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  3. 3.
    Hartigan, J.: Clustering Algorithms. Wiley, New York (1975)MATHGoogle Scholar
  4. 4.
    Park, D.: Centroid Neural Network for Unsupervised Competitive Learning. IEEE Trans. on Neural Networks 11(2), 520–528 (2000)CrossRefGoogle Scholar
  5. 5.
    Park, D.C., Kwon, O.H.: Centroid Neural Network with the Divergence Measure for GPDF Data Clustering. IEEE Trans. on Neural Networks (in review)Google Scholar
  6. 6.
    Daugman, J.G.: Complete Discrete 2D Gabor Transform by Neural Networks for Image Analysis and Compression. IEEE Trans. on Acoust., Speech, and Signal Processing 36, 1169–1179 (1988)MATHCrossRefGoogle Scholar
  7. 7.
    Pun, C.M., Lee, M.C.: Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(9), 1228–1233 (2004)CrossRefGoogle Scholar
  8. 8.
    Huang, Y.L., Chang, R.F.: Texture Features for DCT-Coded Image Retrieval and Classification. In: ICASSP Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3013–3016 (1999)Google Scholar
  9. 9.
    Park, D.C., Woo, Y.: Weighted Centroid Neural Network for Edge Reserving Image Compression. IEEE Trans. on Neural Networks 12, 1134–1146 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-Chul Park
    • 1
  • Chung Nguyen Tran
    • 1
  • Yunsik Lee
    • 2
  1. 1.Dept. of Information EngineeringMyong Ji UniversityKorea
  2. 2.SoC Research CenterKorea Electronics Tech. Inst.SeongnamKorea

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