Multimedia Tools and Applications

, Volume 72, Issue 2, pp 1961–1984 | Cite as

Image retrieval based on quadtree classified vector quantization



In this paper, a color image retrieval scheme based on quadtree classified vector quantization (QCVQ) is proposed. This scheme not only captures intra-block correlation but also exploits the visual importance of image blocks to efficiently describe the content of images in a compressed domain. In the proposed algorithm, a query image is first divided by quadtree segmentation and then classified into smooth and high-detail blocks. For high-detail blocks, the local thresholding classifier with 28 edge binary templates is employed to extract a variety of visually important regions which are edge intensive. After all of the blocks in the image are encoded by the pre-trained QCVQ codebook, the indices in the compressed domain are obtained. Finally, the frequencies of indices are counted to build the index histogram as a feature of the query image. Simulation results demonstrate that our proposed scheme yields the better retrieval performance compared to the well-known vector quantization (VQ)-based image retrieval method and three other techniques. These results show that quadtree segmentation and edge style classification are indeed helpful for improving the performance of content-based image retrieval.


Quadtree segmentation Vector quantization Classified vector quantization Image indexing Content-based image retrieval 


  1. 1.
    Ajay HD, James AS (2005) Content-based image retrieval via vector quantization. Lect Notes Comput Sci 3804:502–509CrossRefGoogle Scholar
  2. 2.
    Arnold WMS, Marcel W, Simone S, Amarnath G, Ramesh J (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380CrossRefGoogle Scholar
  3. 3.
    Brown M, Susstrunk S (2011) Multi-spectral SIFT for scene category recognition. IEEE Conference on Computer Vision and Pattern Recognition 177–184Google Scholar
  4. 4.
    Greg P, Ramin Z, Justin M (1996) Comparing images using color coherence vectors. Proc ACM Multimed 96:65–73Google Scholar
  5. 5.
    Guoping Q (2003) Color image indexing using BTC. IEEE Trans Image Process 12(1):93–101CrossRefGoogle Scholar
  6. 6.
    Hamid AM, Taher TK, Amir HR, Mahdi (2005) S-T Wavelet correlogram: a new approach for image indexing and retrieval. Pattern Recogn 38:2506–2518CrossRefGoogle Scholar
  7. 7.
    Hossein N, Saeid S (2005) Object-based image indexing and retrieval in DCT domain using clustering techniques. Proc World Acad Sci Eng Technol 3Google Scholar
  8. 8.
    Hrovje D, Nikola R, Dinko B, Jurica U (2000) Local thresholding classified vector quantization with memory reduction. Image Signal Process AnalGoogle Scholar
  9. 9.
    Idris F, Panchanathan S (1995) Storage and retrieval of compressed images. IEEE Trans Comput Electron 41(3):937–941Google Scholar
  10. 10.
    Idris F, Panchanathan S (1997) Image and video indexing using vector quantization visual computing and communications laboratoryGoogle Scholar
  11. 11.
    James ZW, Gio W, Oscar F, Sha-Xin W (1997) Wavelet-based image indexing techniques with partial sketch retrieval capability. In: Proceedings of the Fourth Forum on Research and Technology Advances in Digital Libraries 13–24Google Scholar
  12. 12.
    James ZW, Jia L, Gio W (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23:947–963CrossRefGoogle Scholar
  13. 13.
    Jing L, Allinson NM (2008) A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12):1771–1787Google Scholar
  14. 14.
    Jing H, Kumar SR, Mandar M, Wei-Jing Z, Ramin Z (1997) Image indexing using color correlogram. Proc Conf Comp Vision Pattern Recog 97:762–768Google Scholar
  15. 15.
    Kokare M, Biswas PK, Chatterji BN (2007) Texture image retrieval using rotated wavelet filters. Pattern Recogn Lett 28(10):1240–1249CrossRefGoogle Scholar
  16. 16.
    Liapis S, Tziritas G (2004) Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Trans Multimed 6(5):676–686CrossRefGoogle Scholar
  17. 17.
    Lu Z-C, Chang C-C (2007) Color image retrieval technique based on color features and image bitmap. Inf Process Manag 43(2):461–472CrossRefMathSciNetGoogle Scholar
  18. 18.
    Manjunath B, Ohm JR, Vasudevan V, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715CrossRefGoogle Scholar
  19. 19.
    Michael JS, Dana HB (1991) Color indexing. Int J Comput Vis 7(1):11–32CrossRefGoogle Scholar
  20. 20.
    MIT Vision and Modeling Group, Vision Texture. [Online]. Available:
  21. 21.
    Neetu SS, Paresh RS, Jaikaran SS (2011) Efficient CBIR using color histogram processing. Signal Image Process 2(1):94–112Google Scholar
  22. 22.
    Paschos G, Radev I, Prabakar N (2003) Image content-based retrieval using chromaticity moments. IEEE Trans Knowl Data Eng 15(5):1069–1072CrossRefGoogle Scholar
  23. 23.
    Quweider MK, Salari E (1996) Efficient classification and codebook design for CVQ. IEE Proc Vision Image Signal Process 143(6):344–352CrossRefGoogle Scholar
  24. 24.
    Ramamurthi B, Gersho A (1986) Classified vector quantization of images. IEEE Trans Commun 34(11):1105–1115CrossRefGoogle Scholar
  25. 25.
    Robert MG (1982) Vector quantization. IEEE Trans Inf Theory 28:157–166CrossRefGoogle Scholar
  26. 26.
    Salih, ND, Besar R, Abas FS (2011) Multi-level shape description technique systems. Proceedings of the 5th International Conference on IT & Multimedia at UNITENGoogle Scholar
  27. 27.
    Schaefer G (2002) Compressed domain image retrieval by comparing vector quantization codebooks. Visual Commun Image Process 959–966Google Scholar
  28. 28.
    Shyh-Wei T, Guojun L (2007) Image indexing and retrieval based on vector quantization. Pattern Recogn 40(11):3299–3316CrossRefMATHGoogle Scholar
  29. 29.
    Van de Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32(9):1582–1596CrossRefGoogle Scholar
  30. 30.
    Yamazaki T, Fujikawa T, Katto J (2012) Improving the performance of SIFT using bilateral filter and its application to generic object recognition. IEEE Int Conf Acoust Speech Signal Process 945–948Google Scholar
  31. 31.
    Yang SH, Yang SS (1996) New classified vector quantization with quadtree segmentation for image coding. Int Conf Signal Process 2:1051–1054Google Scholar
  32. 32.
    Young R, Thomas SH (1999) Image retrieval: Current techniques, promising, directions, and open issues. J Vis Commun Image Represent 10:39–62CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Graduate Institute of Communication EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Electrical EngineeringTung-Nan UniversityXinbeiTaiwan

Personalised recommendations