A Color-Based Image Retrieval Method Using Color Distribution and Common Bitmap

  • Chin-Chen Chang
  • Tzu-Chuen Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3689)


Image retrieval has emerged as an important problem in multimedia database management. This paper uses the color distribution, the mean value and the standard deviation, of an image as global information for image retrieval. Furthermore, this paper uses the common bitmap to represent the local characteristics of the image. The performance of the method is tested on three different image databases consisting of 410, 235, and 10,235 images. The third database has been partitioned into 10 categories for exploring the category retrieval ability. According to the experimental results, we find that the proposed method can effectively retrieve more similar images than other methods and the category ability is also higher than others. In addition, the total memory space for saving the image features of the proposed method is less than other methods.


Image Retrieval Image Database Query Image Color Histogram Color Distribution 
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.
    Brunelli, R., Mich, O.: Histograms Analysis for Image Retrieval. Pattern Recognition 34, 1625–1637 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Chang, C.C., Chang, Y.K.: A Fast Filter for Image Retrieval Based on Color-Spatial Features. In: Proceedings of the Second International Workshop on Software Engineering and Multimedia Applications, Baden-Baden, Germany, vol. 2, pp. 47–51 (2000)Google Scholar
  3. 3.
    Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by Image and Video Content: the QBIC System, vol. 28(9), pp. 23–32. IEEE Computer, Los Alamitos (1995)Google Scholar
  4. 4.
    Chan, Y.K., Liu, Y.T.: An Image Retrieval System Based on the Image Feature of Color Differences on Edges in Spiral Scan Order. International Journal on Pattern Recognition and Artificial Intelligence 17(8), 1417–1429 (2003)CrossRefGoogle Scholar
  5. 5.
    Du, Y.P., Wang, J.Z.: A Scalable Integrated Region-Based Image Retrieval System. In: Proceedings of the International Conference on Image Processing, Thessaloniki, Greece, vol. 1, pp. 22–25 (2001)Google Scholar
  6. 6.
    Fuh, C.S., Cho, S.W., Essig, K.: Hierarchical Color Image Region Segmentation for Content-Based Image Retrieval System. IEEE Transactions on Image Processing 9(1), 156–162 (2000)CrossRefGoogle Scholar
  7. 7.
    Gong, Y., Chuan, C.H., Xiaoyi, G.: Image Indexing and Retrieval Using Color Histograms. Multimedia Tools and Applications 2, 133–156 (1996)Google Scholar
  8. 8.
    Gagliardi, I., Schettini, R.: A Method for the Automatic Indexing of Color Image for Effective Image Retrieval. The New Review of Hypermedia and Multimedia 3, 201–224 (1997)CrossRefGoogle Scholar
  9. 9.
    Hsieh, J.W., Grimson, W.E.L., Chiang, C.C., Huang, Y.S.: Region-Based Image Retrieval. In: Proceedings of the International Conference on Image Processing, Vancouver, BC, Canada, vol. 1, pp. 77–80 (2000)Google Scholar
  10. 10.
    Iqbal, Q., Aggarwal, J.K.: CIRES: A System for Content-based Retrieval in Digital Image Libraries. In: Proceedings of the Seventh International Conference on Control, Automation, Robotics and Vision, Singapore, pp. 205–210 (2002)Google Scholar
  11. 11.
    Kankanhalli, M.S., Mehtre, B.M., Huang, H.Y.: Color and Spatial Feature for Content-Based Image Retrieval. Pattern Recognition 22(3-4), 323–337 (2001)CrossRefGoogle Scholar
  12. 12.
    Kou, W.J.: Study on Image Retrieval and Ultrasonic Diagnosis of Breast Tumors. Dissertation, Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, R.O.C (2001)Google Scholar
  13. 13.
    Li, J., Wang, J.Z.: Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1075–1088 (2003)Google Scholar
  14. 14.
    Schettini, R., Ciocca, G., Zuffi, S.: A Survey of Methods for Colour Image Indexing and Retrieval in Image Databases. In: MacDonald, L.W., Luo, M.R. (eds.) Color Imaging Science: Exploiting Digital Media. Wiley, J. & Sons Ltd., Chichester (2001)Google Scholar
  15. 15.
    Stehling, R.O., Nascimento, M.A., Falcao, A.X.: An Adaptive and Efficient Clustering-Based Approach for Content-Based Image Retrieval in Image Databases. In: Proceedings of the International Database Engineering and Applications Symposium, Grenoble, France, pp. 356–365 (2001)Google Scholar
  16. 16.
    Stricker, M., Dimai, A.: Spectral Covariance and Fuzzy Regions for Image Indexing. Machine Vision and Applications 10, 66–73 (1997)CrossRefGoogle Scholar
  17. 17.
    Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)CrossRefGoogle Scholar
  18. 18.
    Wang, J.Z., Du, Y.P.: Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries, Roanoke, Virginia, USA, pp. 268–277 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chin-Chen Chang
    • 1
    • 2
  • Tzu-Chuen Lu
    • 2
  1. 1.Department of Information Engineering and Computer ScienceFeng Chia UniversityTaichungTaiwan, R.O.C.
  2. 2.Department of Computer Science and Information EngineeringNational Chung Cheng UniversityChiayiTaiwan, R.O.C.

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