Multimedia Tools and Applications

, Volume 76, Issue 6, pp 7633–7659 | Cite as

Image retrieval based on exponent moments descriptor and localized angular phase histogram

Article

Abstract

Multiple feature extraction and combination is one of the most important issues in the content-based image retrieval (CBIR). In this paper, we propose a new content-based image retrieval method based on an efficient combination of shape and texture features. As its shape features, exponent moments descriptor (EMD), which has many desirable properties such as expression efficiency, robustness to noise, geometric invariance, fast computation etc., is adopted in RGB color space. As its texture features, localized angular phase histogram (LAPH) of the intensity component, which is robust to illumination, scaling, and image blurring, is used in hue saturation intensity (HSI) color space. The combination of above shape and texture information provides a robust feature set for color image retrieval. Experimental results on well known databases show significant improvements in retrieval rates using the proposed method compared with some current state-of-the-art approaches.

Keywords

Content-based image retrieval Exponent moments descriptor Localized angular phase histogram Combination 

References

  1. 1.
    Amanatiadis A, Kaburlasos VG, Gasteratos A, Papadakis SE (2011) Evaluation of shape descriptors for shape-based image retrieval. IET Image Process 5(5):493–499CrossRefGoogle Scholar
  2. 2.
    Anuar FM, Setchi R, Lai Y (2013) Trademark image retrieval using an integrated shape descriptor. Expert Syst Appl 40(1):105–121CrossRefGoogle Scholar
  3. 3.
    Aptoula E (2014) Remote sensing image retrieval with global morphological texture descriptors. IEEE Trans Geoscience Remote Sensing 52(5):3023–3034CrossRefGoogle Scholar
  4. 4.
    Aptoula E, Lefèvre S (2009) Morphological description of color images for content-based image retrieval. IEEE Trans Image Process 18(11):2505–2517MathSciNetCrossRefGoogle Scholar
  5. 5.
    Atto AM, Berthoumieu Y, Bolon P (2013) 2-D wavelet packet spectrum for texture analysis. IEEE Trans Image Process 22(6):2495–2500MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen WT, Liu WC, Chen MS (2010) Adaptive color feature extraction based on image color distributions. IEEE Trans Image Process 19(8):2005–2016MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chun YD, Kim NC, Jang IH (2008) Content-based image retrieval using multiresolution color and texture features. IEEE Trans Multimedia 10(6):1073–1084CrossRefGoogle Scholar
  8. 8.
    Datta R, Joshi D, Li J (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60CrossRefGoogle Scholar
  9. 9.
    Farsi H, Mohamadzadeh S (2013) Colour and texture feature-based image retrieval by using hadamard matrix in discrete wavelet transform. IET Image Process 7(3):212–218MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gholamreza A, Ali E, George B, Mircea N (2005) Accurate and efficient computation of high order Zernike moments. First Int Symp Visual Comput, Lecture Notes Comput Sci 3804:462–469. doi:10.1007/11595755_56 CrossRefGoogle Scholar
  11. 11.
    He Z, You X, Yuan Y (2009) Texture image retrieval based on non-tensor product wavelet filter banks. Signal Process 89(8):1501–1510CrossRefMATHGoogle Scholar
  12. 12.
    Hong C, Yu J, Tao D (2015) Image-based 3d human pose recovery by multi-view locality sensitive sparse retrieval. EEE Trans Industrial Electronics 62(6):3742–3751Google Scholar
  13. 13.
    Hu W, Xie N, Li L, Zeng X (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst, Man Cybernetics, Part C: Appl Rev 41(6):797–819CrossRefGoogle Scholar
  14. 14.
    Jacob IJ, Srinivasagan KG, Jayapriya K (2014) Local oppugnant color texture pattern for image retrieval system. Pattern Recogn Lett 42:72–78CrossRefGoogle Scholar
  15. 15.
    Jian M, Lam KM (2014) Face-image retrieval based on singular values and potential-field representation. Signal Process 100:9–15CrossRefGoogle Scholar
  16. 16.
    Jun Y, Dongquan L, Dacheng T, Hock Soon S (2012) On combining multiple features for cartoon character retrieval and clip synthesis. IEEE Trans Syst, Man Cybernetics, Part B: Cybernetics 42(5):1413–1427CrossRefGoogle Scholar
  17. 17.
    Kashif I, Michael OO, Anne J (2012) Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics. J Comput Syst Sci 78(4):1258–1277MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kokare M, Biswas PK, Chatterji BN (2006) Texture image retrieval using new rotated complex wavelet filters. IEEE Trans Syst Man Cybernetics Part B 35(6):1168–78CrossRefGoogle Scholar
  19. 19.
    Lasmar NE, Berthoumieu Y (2014) Gaussian copula multivariate modeling for texture image retrieval using wavelet transforms. IEEE Trans Image Process 23(5):2246–2261MathSciNetCrossRefGoogle Scholar
  20. 20.
    Li X (2003) Image retrieval based on perceptive weighted color blocks. Pattern Recogn Lett 24(12):1935–1941CrossRefGoogle Scholar
  21. 21.
    Li S, Lee MC, Pun CM (2009) Complex Zernike moments features for shape-based image retrieval. IEEE Trans Systems, Man Cybernetics, Part A: Systems Humans 39(1):227–237CrossRefGoogle Scholar
  22. 22.
    Li C, Li J, Fu B (2013) Magnitude-phase of quaternion wavelet transform for texture representation using multilevel copula. IEEE Signal Proc Letters 20(8):799–802CrossRefGoogle Scholar
  23. 23.
    Liapis S, Tziritas G (2004) Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Trans Multimedia 6(5):676–686CrossRefGoogle Scholar
  24. 24.
    Lin CH, Chen RT, Chan YK (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27(6):658–665CrossRefGoogle Scholar
  25. 25.
    Liu M, Vemuri BC, Amari SI, Nielsen F (2012) Shape retrieval using hierarchical total Bregman soft clustering. IEEE Trans Pattern Analysis Machine Intell 34(12):2407–2419CrossRefGoogle Scholar
  26. 26.
    Liu GH, Yang JY (2013) Content-based image retrieval using color difference histogram. Pattern Recogn 46(1):188–198CrossRefGoogle Scholar
  27. 27.
    Meng M, Ping ZL (2011) Decompose and reconstruct images based on exponential Fourier moments. J Inner Mongolia Normal Univ (Natural Sci Ed) 40(3):258–260Google Scholar
  28. 28.
    Pappas TN, Neuhoff DL, de Ridder H, Zujovic J (2013) Image analysis: focus on texture similarity. Proc IEEE 101(9):2044–2057CrossRefGoogle Scholar
  29. 29.
    Park U, Park J, Jain AK (2014) Robust keypoint detection using higher-order scale space derivatives: application to image retrieval. IEEE Signal Proc Letters 21(8):962–965CrossRefGoogle Scholar
  30. 30.
    Pooja CS (2011) Improving image retrieval using combined features of Hough transform and Zernike moments. Opt Lasers Eng 49(12):1384–1396CrossRefGoogle Scholar
  31. 31.
    Prasad BG, Biswas KK, Gupta SK (2004) Region-based image retrieval using integrated color, shape, and location index. Comput Vis Image Underst 94(1–3):193–233CrossRefGoogle Scholar
  32. 32.
    Rakvongthai Y, Oraintara S (2013) Statistical texture retrieval in noise using complex wavelets. Signal Process Image Commun 28(10):1494–1505CrossRefGoogle Scholar
  33. 33.
    Saipullah KM, Kim DH (2012) A robust texture feature extraction using the localized angular phase. Multimedia Tools Appl 59(3):717–747CrossRefGoogle Scholar
  34. 34.
    Seetharaman K, Jeyakarthic M (2014) Statistical distributional approach for scale and rotation invariant color image retrieval using multivariate parametric tests and orthogonality condition. J Vis Commun Image Represent 25(5):727–739CrossRefGoogle Scholar
  35. 35.
    Sherin MY (2012) ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Comput Electrical Eng 38(5):1358–1376CrossRefGoogle Scholar
  36. 36.
    Shu X, Xiao-Jun W (2011) A novel contour descriptor for 2D shape matching and its application to image retrieval. Image Vis Comput 29(4):286–294CrossRefGoogle Scholar
  37. 37.
    Singha M, Hemachandran K, Paul A (2012) Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram. IET Image Process 6(9):1221–1229MathSciNetCrossRefGoogle Scholar
  38. 38.
    Susana A, Annaa S, Maria V, Xavier O (2012) Low-dimensional and comprehensive color texture description. Comput Vis Image Underst 116(1):54–67CrossRefGoogle Scholar
  39. 39.
    Talib A, Mahmuddin M, Husni H (2013) A weighted dominant color descriptor for content-based image retrieval. J Vis Commun Image Represent 24(3):345–360CrossRefGoogle Scholar
  40. 40.
    Van De Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Analysis Machine Intell 32(9):1582–1596CrossRefGoogle Scholar
  41. 41.
    J. Wan, D. Wang, S. C. Hoi (2014) Deep learning for content-based image retrieval: a comprehensive study. Proceedings of the ACM International Conference on Multimedia. Orlando, FL, USA, 2014: 157–166Google Scholar
  42. 42.
    Wang XY, Yu YJ, Yang HY (2011) An effective image retrieval scheme using color, texture and shape features. Comput Standards Interfaces 33(1):59–68CrossRefGoogle Scholar
  43. 43.
    Yap PT, Paramesran R (2006) Content-based image retrieval using Legendre chromaticity distribution moments. IEE Proc-Vision, Image Signal Proc 153(1):17–24CrossRefGoogle Scholar
  44. 44.
    Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recogn 37(1):1–19CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianPeople’s Republic of China

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