Advertisement

Image Retrieval Using Low Level Features of Object Regions with Application to Partially Occluded Images

  • E. R. Vimina
  • K. Poulose Jacob
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

This paper proposes an image retrieval system using the local colour and texture features of object regions and global colour features of the image. The object regions are roughly identified by segmenting the image into fixed partitions and finding the edge density in each partition using edge thresholding and morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region and Euclidean distance measure is used for computing the distance between the features of the query and target image. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods. Also promising results are obtained for 50% and 75% occluded query images.

Keywords

Content Based Image Retrieval GLCM Colour histogram 

References

  1. 1.
    Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 911–920 (2006)Google Scholar
  2. 2.
    Chen, Y., Wang, J.Z., Krovetz, R.: CLUE: Cluster-based retrieval of images by unsupervised learning. IEEE Transactions on Image Processing 14(8), 1187–1201 (2005)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., et al.: Query by Image and Video Content: The QBIC System. IEEE Computer 28, 23–32 (1995)CrossRefGoogle Scholar
  5. 5.
    Pentland, A., Picard, R., Sclaroff, S.: Photobook.: Content-based Manipulation of Image Databases. In: Proc. SPIE Storage and Retrieval for Image and Video Databases II, SanJose, CA, pp. 34–47 (1994)Google Scholar
  6. 6.
    Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J., Blobworld: A System for Region-Based Image Indexing and Retrieval. In: Proc. Visual Information Systems, pp. 509–516 (1999)Google Scholar
  7. 7.
    Haralick, R.M., Shanmugan, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3, 610–621 (1973)Google Scholar
  8. 8.
    Murala, S., Gonde, A.B., Maheshwari, R.P.: Color and Texture Features for Image Indexing and Retrieval. In: 2009 IEEE International Advance Computing Conference (IACC 2009), pp. 1411–1416 (2009)Google Scholar
  9. 9.
  10. 10.
    Huang, P.W., Dai, S.K.: Image retrieval by texture similarity. Pattern Recognition 36(3), 665–679 (2003)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jhanwar, N., Chaudhuri, S., Seetharaman, G., Zavidoviqu, B.: Content based image retrieval using motif co-occurrence matrix. Image Vis. Computing 22(14), 1211–1220 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • E. R. Vimina
    • 1
  • K. Poulose Jacob
    • 2
  1. 1.Department of Computer ScienceRajagiri College of Social SciencesKochiIndia
  2. 2.Department of Computer ScienceCochin University of Science and TechnologyKochiIndia

Personalised recommendations