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Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval

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Abstract

Shape, being an important part of an object, has a special place in the field of shape-based image retrieval (SBIR). To retrieve most appropriate images, various descriptors are applied in SBIR like Zernike moments (ZMs), complex Zernike moments (CZMs) etc. Though ZMs/CZMs are good in SBIR but they are capable of extracting only global details of an image, hence something in addition to this is desirable to improve the performance of SBIR system. This paper presents experimental analysis of pixel-based dense descriptors such as local binary pattern (LBP), local directional pattern (LDP) and their variants. These descriptors are used as local features along with ZMs global features in achieving higher and accurate retrieval rate in SBIR system. We have analyzed these variants of LBP/LDP with various similarity measures on images. In case of ZMs, the magnitude component is used as global features. These methods are tested separately on suitable shape databases. Various databases used in the paper are MPEG-7 CE-2 region-based database, MPEG-7 CE-1 contour-based database and Trademark database. It can be concluded from the experimental analysis that the performance of LDP along with ZMs is better than that of ZMs alone and of ZMs along with other variants of LBP and LDP.

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References

  1. Smeulders A.W.M., Worring M., Santini S., Gupta A., Jain R.: Content-based image retrieval at the end of the early years. In: IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1379 (2000)

    Google Scholar 

  2. Rui Y., Huang Thomas S.: Image retrieval: current techniques, promising directions and open issues. J. Vis. Commun. Image Represent. 10, 39–62 (1999)

    Article  Google Scholar 

  3. Eakins J.P., Graham M.E.: Content-Based Image Retrieval: A Report to the JISC Technology Application Programme. Institute for Image Data Research, University of Northumbria at Newcastle, UK (1999)

    Google Scholar 

  4. Qiu G., Morris J., Fan X.: Visual guided navigation for image retrieval. Pattern Recogn. 40(6), 1711–1721 (2007)

    Article  MATH  Google Scholar 

  5. Datta R., Joshi D., Li J., Wang J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 1–60 (2008)

    Article  Google Scholar 

  6. ElAlami M.E.: A novel image retrieval model based on most relevant features. Knowl. Based Syst. 24(1), 23–32 (2011)

    Article  Google Scholar 

  7. Liu Y., Zhang D., Lu G., Ma W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40(1), 262–282 (2007)

    Article  MATH  Google Scholar 

  8. Papadopoulos G.T., Saathoff C., Escalante H.J., Mezaris V., Kompatsiaris I., Strintzis M.G.: A comparative study of object-level spatial context techniques for semantic image analysis. Comput. Vis. Image Underst. 115(9), 1288–1307 (2011)

    Article  Google Scholar 

  9. Tang J., Li H., Qi G., Chua T.: Image annotation by graph-based inference with integrated multiple/single instance representations. In: IEEE Transact. Multimed. 12(2), 131–141 (2010)

    Google Scholar 

  10. Oussalah, M.: Content based image retrieval: review of state of art and future directions. In: Proceedings of First Workshops on Image Processing Theory, Tools and Applications IPTA 2008, November 23–26, 2008, pp. 1–10 (2008)

  11. Loncaric S.: A survey of shape analysis techniques. Pattern Recogn. 31, 983–1001 (1998)

    Article  Google Scholar 

  12. Yang, M., Kpalma, K., Ronsin, J.: A survey of shape feature extraction techniques. Pattern Recogn. (Issue Nov.), pp. 43–90 (2008)

  13. Mehtre B.M., Kankanhalli M.S., Lee W.F.: Shape measures for content based image retrieval: a comparison. Inf. Process. Manage. 33(3), 319–337 (1997)

    Article  Google Scholar 

  14. Veltkamp, R.C.: Shape matching: similarity measures and algorithms. In: Proceedings of International Conference on Shape Modeling and Applications, IEEE, Piscataway, NJ, pp. 188–197 (2001)

  15. Zhang D., Lu G.: Review of shape representation and description techniques. Pattern Recogn. 37, 1–19 (2004)

    Article  MATH  Google Scholar 

  16. Kim W.Y., Kim Y.S.: A region based shape descriptor using Zernike moments. J. Signal Process. Image Commun. 16, 95–102 (2000)

    Article  Google Scholar 

  17. Teh C.H., Chin R.T.: On image analysis by the methods of moments. In: IEEE Trans. Pattern Anal. Mach. Intell. 10(4), 496–513 (1998)

    Google Scholar 

  18. Zhang D., Lu G.: Shape-based image retrieval using generic Fourier descriptor. J. Signal Process. Image Commun. 17(10), 825–848 (2002)

    Article  Google Scholar 

  19. Zhang, D., Lu, G.: A Comparative Study of Three Region Shape Descriptors. Book Digital Image Computing—Techniques and Applications, pp. 21–22 (2002)

  20. Goyal, A., Walia, E., Sainim, H.S.: Improved accuracy in shape based image retrieval with complex Zernike moments using wavelets. In: Proceedings of 2nd International Congress on Image and Signal Processing (CISP 2009), Tianjin, China vol. 5, pp. 2247–2251 (2009)

  21. Wei C.H., Li Y., Chau W.-Y., Li C.-T.: Trademark image retrieval using synthetic features for describing global shape and interior structure. Pattern Recogn. 42, 386–394 (2008)

    Article  Google Scholar 

  22. Qi H., Li K., Shen Y.-M., Qu W.-Y.: An effective solution for trademark image retrieval by combinineg shape description and feature matching. Pattern Recogn. 43(6), 2017–2027 (2010)

    Article  MATH  Google Scholar 

  23. Yadav R.B., Nishchal N.K., Gupta A.K., Rastogi V.K.: Retrieval and classification of objects using generic Fourier, Legendre moment, and wavelet Zernike moment descriptors and recognition using joint transform correlator. Opt. Lasers Technol. 40, 517–527 (2008)

    Article  Google Scholar 

  24. Goyal A., Walia E., Saini H.S.: Enhanced retrieval accuracy with ZMs using dual tree complex wavelets and Fourier features. ICGST Int. J. Graphics Vis. Image Process. (GVIP) 10(3), 27–34 (2010)

    Google Scholar 

  25. Li S., Lee M.C., Pun C.M.: Complex Zernike moments features for shape-based image retrieval. In: IEEE Trans. Syst. Man Cybern. 39(1), 227–237 (2009)

    Google Scholar 

  26. Li J., Allinson N.M.: A comprehensive review of current local features for computer vision. Neurocomputing 71, 1771–1787 (2008)

    Article  Google Scholar 

  27. Heikkilä M., Pietikäinen M., Schmid C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)

    Article  MATH  Google Scholar 

  28. Ojala T., Pietikäinen M., Harwood D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  29. Ojala T., Pietikäinen M., Mäenpää T.: Multiresolution gray scale and rotation invariant texture analysis with local binary patterns. In: IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Google Scholar 

  30. Ahonen T., Hadid A., Pietikäinen M.: Face description with local binary patterns: application to face recognition. In: IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Google Scholar 

  31. Huang D., Shan C., Ardabilian M., Wang Y., Chen L.: Local binary patterns and its application to facial image analysis: a survey. In: IEEE Trans. Syst. Man Cybernet. Part C Appl. Rev. 41(4), 1–17 (2011)

    Google Scholar 

  32. Gho Z., Zhang L., Zhang G.: A completed modeling of local binary pattern operator for texture classification. In: IEEE Trans. Image Proc. 19(6), 1657–1663 (2010)

    Google Scholar 

  33. Jabid, T., Kabir, Md.H., Chae, O.: Local directional pattern(LDP) for face recognition. In: Proceedings of IEEE International Conference on Consumer Electronics, Las Vegas, NV, January 2010, pp. 329–330 (2010)

  34. Revaud J., Lavoue G., Baskurt A.: Improving Zernike moments comparison for optimal similarity and rotation angle retrieval. In: IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 627–636 (2009)

    Google Scholar 

  35. Broumandnia A., Shanbehzadeh J.: Fast Zernike wavelet moments for Farsi character recognition. Image Vis. Comput. 25, 717–726 (2007)

    Article  Google Scholar 

  36. Singh C.: Improved quality of reconstructed images using floating point arithmetic for moment calculation. Pattern Recogn. 39, 2047–2064 (2006)

    Article  MATH  Google Scholar 

  37. Kan C., Srinath M.D.: Invariant character recognition with Zernike and orthogonal Fourier–Mellin moments. Pattern Recogn. 35, 143–154 (2002)

    Article  MATH  Google Scholar 

  38. Abdallah S.M., Nebot E.M., Rye D.C.: Object recognition and orientation via Zernike moments. Lect. Notes Comput. Sci. Comput. Vis. ACCV’ 98(1351/1997), 386–393 (1997)

    Article  Google Scholar 

  39. Chen Z., Sun S.K.: A Zernike moment phase-based descriptor for local image representation and matching. In: IEEE Trans. Image Process. 19(1), 205–219 (2010)

    Google Scholar 

  40. Teague M.R.: Image analysis via the general theory of moments. J. Opt. Soc. Am. 70(8), 920–930 (1980)

    Article  MathSciNet  Google Scholar 

  41. Wee C.Y., Raveendran P.: On the computational aspects of Zernike moments. Image Vis. Comput. 25(6), 967–980 (2007)

    Article  Google Scholar 

  42. Ferrer, M.A., Vargas, F., Travieso, C.M., Alonso, J.B.: Signature verification using local directional pattern (LDP). In: Proceedings of IEEE International Carnahan Conference on Security Technology (ICCST), 2010, 5–8 October, pp. 336–340 (2010)

  43. Burcin K., Nabiyev V.V.: Down syndrome recognition using local binary patterns and statistical evaluation of the system. Expert Syst. Appl. 38(7), 8690–8695 (2011)

    Article  Google Scholar 

  44. Kabir, Md.H., Jabid, T., Chae, O.: Local directional pattern (LDP): a robust image descriptor for object recognition. In: Proceedings of IEEE International Conference Advances Video and Signal-Based Surveillance, pp. 482–487 (2010)

  45. Jabid, T., Kabir, Md.H., Chae, O.: Robust facial expression recognition based on local directional pattern. ETRI J. (2010)

  46. Mu, Y.D., Yan, S.C., Liu, Y., Huang, T., Zhou, B.F.: Discriminative local binary patterns for human detection in personal album. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2008, 23–28 June 2008, pp. 1–8 (2008)

  47. Su S.-Z., Chen S.-Y., Li S.-Z., Li S.-A., Duh D.-J.: Structured local binary Haar pattern for pixel-based graphics retrieval. Electron. Lett. 46(14), 996–998 (2010)

    Article  Google Scholar 

  48. Guo Z., Zhang L., Zhang D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn. 43(3), 706–719 (2010)

    Article  MATH  Google Scholar 

  49. Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. In: Proceedings of Lecture Notes in Computer Science, SCIA, pp. 61–70 (2009)

  50. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In: Proceedings of International Conference Computer Vision, Graphics Image Processing, pp. 58–69 (2006)

  51. Mallat S.: A Wavelet Tour of Signal Processing. Academic Press, New York (1999)

    MATH  Google Scholar 

  52. Kabir, Md.H., Jabid, T., Chae, O.: A Local Directional Pattern Variance (LDPv) based face descriptor for human facial expression recognition. In: Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS (2010)

  53. Goyal A., Walia E.: An analysis of shape based image retrieval using variants of Zernike moments as features. Int. J. Imag. Robotics (CESER publications) 7(S12), 44–69 (2012)

    Google Scholar 

  54. Kim, W.-Y., Kim, Y.-S.: A new region-based shape descriptor. ISO/IECMPEG99/M5472, TR15-01, Maui, Hawaii, December (1999)

  55. Bober M.: mpeg-7 Visual Shape Descriptors. In: IEEE Trans. Circ. Syst. Vid. Technol 1(6), 716–719 (2001)

    Google Scholar 

  56. Hung M.H., Hsieh C.H., Kuo C.M.: Similarity retrieval of shape images based on database classification. J. Vis. Commun. Image Represent. 17(5), 970–985 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Applications, Guru Nanak Institute of Management and Technology, Ludhiana, Punjab, India

    Anjali Goyal

  2. Department of Computer Science, South Asian University, New Delhi, India

    Ekta Walia

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  1. Anjali Goyal
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  2. Ekta Walia
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Correspondence to Anjali Goyal.

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Goyal, A., Walia, E. Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval. SIViP 8, 1273–1289 (2014). https://doi.org/10.1007/s11760-012-0353-x

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  • DOI: https://doi.org/10.1007/s11760-012-0353-x

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