[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

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

  • Conference paper
Information Retrieval Technology (AIRS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3689))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Brunelli, R., Mich, O.: Histograms Analysis for Image Retrieval. Pattern Recognition 34, 1625–1637 (2001)

    Article  MATH  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. Stricker, M., Dimai, A.: Spectral Covariance and Fuzzy Regions for Image Indexing. Machine Vision and Applications 10, 66–73 (1997)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chang, CC., Lu, TC. (2005). A Color-Based Image Retrieval Method Using Color Distribution and Common Bitmap. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_5

Download citation

  • DOI: https://doi.org/10.1007/11562382_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29186-2

  • Online ISBN: 978-3-540-32001-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics