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Robust image retrieval based on color histogram of local feature regions

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Abstract

Color histograms lack spatial information and are sensitive to intensity variation, color distortion and cropping. As a result, images with similar histograms may have totally different semantics. The region-based approaches are introduced to overcome the above limitations, but due to the inaccurate segmentation, these systems may partition an object into several regions that may have confused users in selecting the proper regions. In this paper, we present a robust image retrieval based on color histogram of local feature regions (LFR). Firstly, the steady image feature points are extracted by using multi-scale Harris-Laplace detector. Then, the significant local feature regions are ascertained adaptively according to the feature scale theory. Finally, the color histogram of local feature regions is constructed, and the similarity between color images is computed by using the color histogram of LFRs. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images. Especially, it is robust to some classic transformations (additive noise, affine transformation including translation, rotation and scale effects, partial visibility, etc.).

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References

  1. Castelli V, Bergman LD (2002) Image Databases: Search and Retrieval of Digital Imagery. Wiley, New York

    Google Scholar 

  2. Christos T, Nikolaos AL, George E, Spiros F (2005) A generic scheme for color image retrieval based on the multivariate Wald-Wolfowitz test. IEEE Trans. on Knowledge and Data Engineering 17(6):808–819

    Article  Google Scholar 

  3. Corel Corp. http://www.corel.com

  4. Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys 40(2):1–60

    Article  Google Scholar 

  5. Deselaers T, Keysers D, Ney H (2008) Features for image retrieval: an experimental comparison. Information Retrieval 11(2):77–107

    Article  Google Scholar 

  6. Ediz S, Ugur G, Ulusoy O (2005) A histogram-based approach for object-based query-by-shape-and-color in image and video databases. Image and Vision Computing 23:1170–1180

    Article  Google Scholar 

  7. Gong Y, Chuan CH, Xiaoyi G (1996) Image indexing and retrieval using color histograms. Multimedia Tools and Application 2:133–156

    Google Scholar 

  8. Halawani A, Burkhardt H (2004) Image retrieval by local evaluation of nonlinear kernel functions around salient points. In : Proceedings of the 17th International Conference on Pattern Recognition(ICPR 2004): 955–960

  9. Han J, Ma KK (2002) Fuzzy colour histogram and its use in color image retrieval. IEEE Trans. on Image Processing 11(8):944–952

    Article  Google Scholar 

  10. Ju H, Ma KK (2002) Fuzzy color histogram and its use in color image retrieval. IEEE Trans. on Image Processing 11(8):944–952

    Article  Google Scholar 

  11. Lee Hae-Yeoun et al. (2005) Evaluation of feature extraction techniques for robust watermarking. 4th International Workshop, International Workshop on Digital Watermarking 2005(IWDW 2005), Siena, Italy, September 15–17, 2005, Lecture Notes in Computer Science 3710, Springer: 418–431

  12. Lu TC, Chang CC (2007) Color image retrieval technique based on color features and image bitmap. Information Processing and Management 43(2):461–472

    Article  MathSciNet  Google Scholar 

  13. Michael SL, Nice S, Chababe D, Ramsesh J (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. on Multimedia Computing, Communications and Applications 2(1):1–19

    Article  Google Scholar 

  14. Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. International Journal of Computer Vision 60(1):63–86

    Article  Google Scholar 

  15. Mustaffa MR, Ahmad F, Wirza R (2008) Content-based image retrieval based on color-spatial features. Malaysian Journal of Computer Science 21(1):1–12

    Google Scholar 

  16. Paschos G, Radev I, Prabakar N (2003) Image content-based retrieval using chromaticity moments. IEEE Trans. on Knowledge and Data Eng. 15(5):069–1072

    Article  Google Scholar 

  17. Salembier P, Sikora T (2002) Introduction to MPEG-7: Multimedia content description interface. Wiley, New York

    Google Scholar 

  18. Sebe N, Lew MS (2001) Salient points for content-based retrieval. Proceedings. of the British Machine Vision Conference: 401–410.

  19. Siggelkow S (2002) Feature historgrams for content-based image retrieval. PhD thesis, Albert-Ludwigs-Universit¨at, Freiburg, December 2002.

  20. Siggelkow S, Schael M, Burkhardt H. (2001) SIMBA—search iMages by appearance. In B. Radig and S. Florczyk, editors, Proceedings of 23rd DAGM Symposium, number 2191 in LNCS Pattern Recognition, springer, September 2001: 9–16.

  21. Stöttinger J, Sebe N, Gevers T, Hanbury A (2007) Colour interest points for image retrieval. In: Proceedings of the 12th Computer Vision Winter Workshop: 83–90

  22. Xuelong L (2003) Image retrieval based on perceptive weighted color blocks. Pattern Recognition Letters 24(12):1935–1941

    Article  Google Scholar 

  23. Stricker M, Dimai A (1996) Color indexing with weak spatial constraints. SPIE Proc. 2670:29–40

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, China

    Xiang-Yang Wang, Jun-Feng Wu & Hong-Ying Yang

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Xiang-Yang Wang

Authors
  1. Xiang-Yang Wang
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  2. Jun-Feng Wu
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  3. Hong-Ying Yang
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Corresponding author

Correspondence to Xiang-Yang Wang.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 60773031 & 60873222, the Open Foundation of State Key Laboratory of Networking and Switching Technology of China under Grant No. SKLNST-2008-1-01, the Open Foundation of State Key Laboratory of Information Security of China under Grant No. 03-06, the Open Foundation of State Key Laboratory for Novel Software Technology of China under Grant No. A200702, and Liaoning Research Project for Institutions of Higher Education of China under Grant No. 2008351.

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Wang, XY., Wu, JF. & Yang, HY. Robust image retrieval based on color histogram of local feature regions. Multimed Tools Appl 49, 323–345 (2010). https://doi.org/10.1007/s11042-009-0362-0

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  • DOI: https://doi.org/10.1007/s11042-009-0362-0

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