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Efficient transform-based texture image retrieval techniques under quantization effects

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Abstract

With the great demand for storing and transmitting images as well as their managing, the retrieval of compressed images is a field of intensive research. While most of the works have been devoted to the case of losslessly encoded images (by extracting features from the unquantized transform coefficients), new studies have shown that lossy compression has a negative impact on the performance of conventional retrieval systems. In this work, we investigate three different quantization schemes and propose for each one an efficient retrieval approach. More precisely, the uniform quantizer, the moment preserving quantizer and the distribution preserving quantizer are considered. The inherent properties of each quantizer are then exploited to design an efficient retrieval strategy, and hence, to reduce the drop of retrieval performances resulting from the quantization effect. Experimental results, carried out on three standard texture databases and a color dataset, show the benefits which can be drawn from the proposed retrieval approaches.

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Notes

  1. http://vismod.media.mit.edu/vismod/imagery/VisionTexture/

  2. http://www.outex.oulu.fi/

  3. http://www.wavelab.at/sources

  4. http://wang.ist.psu.edu/docs/home.shtml

References

  1. MIT vision and modeling group. vision texture. http://vismod.www.media.mit.edu. [Online]

  2. Agarwal S, Verma AK, Singh P (2013) Content based image retrieval using discrete wavelet transform and edge histogram descriptor. In: IEEE International conference in information systems and computer networks, pp 19–23

  3. Allili MS (2012) Wavelet modeling using finite mixtures of generalized Gaussian distributions: application to texture discrimination and retrieval. IEEE Trans Image Process 21(4):1452–1464

  4. Au KM, Law NF, Siu WC (2007) Unified feature analysis in JPEG and JPEG 2000-compressed domains. Pattern Recogn 40(7):2049–2062

    Article  MATH  Google Scholar 

  5. Belalia A, Belloulata K, Kpalma K (2015) Region-based image retrieval in the compressed domain using shape-adaptive DCT. Multimedia Tools Appl:1–25

  6. Calderbank A, Daubechies I, Sweldens W, Yeo BL (1998) Wavelet transforms that map integers to integers. Appl Comput Harmon Anal 5(3):332–369

    Article  MathSciNet  MATH  Google Scholar 

  7. Chaker A, Kaaniche M, Benazza-Benyahia A (2012) An improved image retrieval algorithm for JPEG 2000 compressed images. In: IEEE International Symposium on Signal Processing and Information Technology. Ho Chi Minh City, Vietnam, pp 1–6

  8. Chaker A, Kaaniche M, Benazza-Benyahia A (2013) An efficient retrieval strategy for wavelet-based quantized images. In: IEEE International Conference on Acoustics Speech and Signal Processing, Vancouver, BC, Canada, pp 1493–1497

  9. Chang CC, Chuang JC, Hu YS (2004) Retrieving digital images from a JPEG compressed image database. Image Vis Comput 22(6):471–484

    Article  Google Scholar 

  10. Choy SK, Tong CS (2010) Statistical wavelet subband characterization based on generalized Gamma density and its application in texture retrieval. IEEE Trans Image Process 19(2):281–289

    Article  MathSciNet  MATH  Google Scholar 

  11. Climer S, Bhatia SK (2002) Image database indexing using JPEG coefficients. Pattern Recogn 35(11):2479–2488

    Article  MATH  Google Scholar 

  12. Datta R, Joshi D, Li J, Wang JZ (2006) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 39:2007

    Google Scholar 

  13. Delp EJ, Mitchell OR (1979) Image compression using block truncation coding. IEEE Trans Commun 27(9):1335–1342

    Article  Google Scholar 

  14. Delp EJ, Mitchell OR (1991) Moment preserving quantization. IEEE Trans Commun 39(11):1549–1558

    Article  Google Scholar 

  15. Do MN, Vetterli M (2002) Wavelet-based texture retrieval using generalized gaussian density and Kullback-Leibler distance. IEEE Trans Image Process 11(2):146–158

    Article  MathSciNet  Google Scholar 

  16. Edmundson D, Schaefer G (2012) Fast JPEG image retrieval using optimised huffman tables. In: International conference on pattern recognition (ICPR), pp 3188–3191

  17. Edmundson D, Schaefer G (2012) Recompressing images to improve image retrieval performance. In: IEEE International conference on acoustics, speech and signal processing, Kyoto, Japan, 4 pages

  18. Edmundson D, Schaefer G, Celebi ME (2012) Robust texture retrieval of compressed images. In: IEEE International conference on image processing, Orlando, USA, pp 2421–2424

  19. Guldogan E, Guldogan O, Kiranyaz S, Caglar K, Gabbouj M (2003) Compression effects on color and texture based multimedia indexing and retrieval. In: IEEE International conference on image processing, Barcelona, Spain, pp 9–12

  20. Guocan F, Jianmin J (2003) JPEG compressed image retrieval via statistical features. Pattern Recogn 36(4):977–985

    Article  Google Scholar 

  21. Jackson D (2004) Fourier series and orthogonal polynomials. Dover Publications

  22. Klejsa J, Zhang G, Li M, Kleijn WB (2013) Multiple description distribution preserving quantization. IEEE Trans Sig Process 61(24):6410–6422

    Article  MathSciNet  Google Scholar 

  23. Kuo YH, Cheng WH, Lin HT, Hsu WH (2012) Unsupervised semantic feature discovery for image object retrieval and tag refinement. IEEE Trans Multimedia 14(4):1079–1090

    Article  Google Scholar 

  24. Kwitt R, Meerwald P Salzburg texture image database. http://www.wavelab.at/sources. [Online]

  25. Kwitt R, Uhl A (2010) Lightweight probabilistic texture retrieval. IEEE Trans Image Process 19(1):241–253

    Article  MathSciNet  MATH  Google Scholar 

  26. Lasmar N, Berthoumieu Y (2014) Gaussian copula multivariate modeling for texture image retrieval using wavelet transforms. IEEE Trans Image Process 23 (5):2246–2261

    Article  MathSciNet  MATH  Google Scholar 

  27. Lay JA, Guan L (1999) Image retrieval based on energy histograms of the low frequency DCT coefficients. In: International conference on acoustics, speech, and signal processing, vol 6, pp 3009–3012

  28. Li M (2011) Distribution preserving quantization. Ph.D. dissertation, KTH Royal Institute of Technology

  29. Li M, Klejsa J, Kleijn WB (2010) Distribution preserving quantization with dithering and transformation. IEEE Signal Process Lett 17(12):1014–1017

    Article  Google Scholar 

  30. Liu D, Liu G, Yu M, Wang Y (2008) An image retrieval method based on tree-structured wavelet transform. In: International conference on computer science and software engineering, vol 4, pp 536– 539

  31. Mandal MK, Aboulnasr T, Panchanathan S (1996) Image indexing using moments and wavelets. IEEE Consumer Electr 42(3):557–565

    Article  Google Scholar 

  32. Mandal MK, Liu C (2003) Efficient image indexing techniques in the JPEG 2000 domain. J Electron Imaging 13(1):182–190

    Article  Google Scholar 

  33. Mathiassen JR, Skavhaug A, Bø K (2002) Texture similarity measure using Kullback-Leibler divergence between Gamma distributions. In: Computer Vision-ECCV 2002. Springer, pp 133–147

  34. Mezaris V, Kompatsiaris I, Boulgouris NV, Strintzis MG (2004) Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval. IEEE Trans Circ Syst Video Technol 14(5):600–621

    Article  Google Scholar 

  35. Muller F (1993) Distribution shape of two-dimensional DCT coefficients of natural images. Electron Lett 29(22):1935–1936

    Article  Google Scholar 

  36. Nadarajah S (2005) A generalized normal distribution. J Appl Stat 32(7):685–694

    Article  MathSciNet  MATH  Google Scholar 

  37. Ngo CW, Pong TC, Chin RT (2001) Exploiting image indexing techniques in DCT domain. Pattern Recogn 34(9):1841–1851

    Article  MATH  Google Scholar 

  38. Ojala T, Maenpaa T, Pietikainen M, Viertola J, Kyllonen J, Huovinen S (2002) Outex-new framework for empirical evaluation of texture analysis algorithms, vol 1, pp 701–706

  39. Rabbani M, Joshi R (2002) An overview of the JPEG 2000 still image compression standard. Signal Process Image Commun 17(1):3–48

    Article  Google Scholar 

  40. Rao KR, Yip P (1990) Discrete cosine transform: algorithms, advantages, applications. Academic Press

  41. Rui Y, Huang TS (1999) Image retrieval: Current techniques, promising directions, and open issues. J Vis Commun Image Represent 10:39–62

    Article  Google Scholar 

  42. Sakji-Nsibi S, Benazza-Benyahia A (2008) Indexing of multichannelimages in the wavelet transform domain. In: IEEE International conference on communication technologies: Theory & practice, Damascus, Syria, pp 1–6

  43. Sakji-Nsibi S, Benazza-Benyahia A (2009) Copula-based statistical models for multicomponent image retrieval in the wavelet tranform domain. In: IEEE International conference on image processing, Cairo, Egypt, pp 253–256

  44. Schaefer G (2008) Does compression affect image retrieval performance? Int J Imaging Syst Technol 18(2-3):101–112

    Article  Google Scholar 

  45. Schuchman L (1964) Dither signals and their effect on quantization noise. IEEE Trans Commun Technol 12(4):162–165

    Article  Google Scholar 

  46. Sengur A (2008) Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification. Expert Syst Appl 34(3):2120–2128

    Article  Google Scholar 

  47. Shoham Y, Gersho A (1988) Efficient bit allocation for an arbitrary set of quantizers. IEEE Trans Acoust Speech Signal Process 36(9):1445–1453

    Article  MATH  Google Scholar 

  48. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. J Vis Commun Image Represent 12:1349–1380

    Google Scholar 

  49. Smith JR, Chang SF (1994) Transform features for texture classification and discrimination in large image databases. In: IEEE International Conference on Image Processing, vol 3, Austin, TX, USA, pp 407–411

  50. Sweldens W (1996) The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl Comput Harmon Anal 3(2):186–200

    Article  MathSciNet  MATH  Google Scholar 

  51. Szegö G (1939) Orthogonal polynomials, vol 23. Amer Mathematical Society

  52. Taubman D, Marcellin M (2001) JPEG2000: Image Compression fundamentals, standards and practice. Kluwer Academic Publishers, Norwell

    Google Scholar 

  53. Tsuhan C, Holmdel NJ (1994) Elimination of subband-coding artifacts using the dithering technique. In: International conference on image processing, vol 2, pp 874–877

  54. Verdoolaege G, Scheunders P (2011) Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination. Int J Comput Vis 95(3):265–286

    Article  MATH  Google Scholar 

  55. Verdoolaege G, De Backer S, Scheunders P (2008) Multiscale colour texture retrieval using the geodesic distance between multivariate generalized gaussian models. In: IEEE International conference on image processing, pp 169–172

  56. Voulgaris G, Jiang J (2001) Texture-based image retrieval in wavelets compressed domain. In: International conference on image processing, vol 2, pp 125–128

  57. Wallace GK (1992) The JPEG still picture compression standard. IEEE Trans Consum Electron 38(1):xviii–xxxiv

    Article  Google Scholar 

  58. Wang CY, Zhang X, Shan R, Zhou X (2015) Grading image retrieval based on DCT and DWT compressed domains using low-level features. J Commun 10 (1):64–73

    Article  Google Scholar 

  59. Wouwer GV, Scheunders P, Dyck DV (1999) Statistical texture characterization from discrete wavelet representations. IEEE Trans Image Process 8(4):592–598

    Article  Google Scholar 

  60. Zargari F, Mosleh A, Ghanbari M (2008) A fast and efficient compressed domain JPEG 2000 image retrieval method. IEEE Trans Consum Electron 54(4):1886–1893

    Article  Google Scholar 

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Chaker, A., Kaaniche, M., Benazza-Benyahia, A. et al. Efficient transform-based texture image retrieval techniques under quantization effects. Multimed Tools Appl 77, 1–25 (2018). https://doi.org/10.1007/s11042-016-4205-5

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