International Journal of Computer Vision

, Volume 98, Issue 2, pp 187–201 | Cite as

Rotation Invariant Curvelet Features for Region Based Image Retrieval

  • Dengsheng Zhang
  • M. Monirul Islam
  • Guojun Lu
  • Ishrat Jahan Sumana


There have been much interest and a large amount of research on content based image retrieval (CBIR) in recent years due to the ever increasing number of digital images. Texture features play a key role in CBIR. Many texture features exist in literature, however, most of them are neither rotation invariant nor robust to scale and other variations. Texture features based on Gabor filters have been shown with significant advantages over other methods, and they are adopted by MPEG-7 as one of the texture descriptors for image retrieval. In this paper, we propose a rotation invariant curvelet features for texture representation. With systematic analysis and rigorous experiments, we show that the proposed curvelet texture features significantly outperforms the widely used Gabor texture features. A novel region padding method is also proposed to apply curvelet transform to region based image retrieval. Retrieval results from standard image databases show that curvelet features are promising for both texture and region representation.


Texture Image retrieval Curvelet transform CBIR Gabor filters 


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  1., accessed on 23rd December (2008).
  2. Arivazhagan, S., Ganesan, L., & Kumar, T. G. S. (2006). Texture classification using Curvelet statistical and co-occurrence features. In Proc. of the 18th international conference on pattern recognition (ICPR06), Washington, DC, August 20–24 (Vol. 2, pp. 938–941). Google Scholar
  3. Bhagavathy, S., & Chhabra, K. (2007). A wavelet-based image retrieval system (Technical Report—ECE278A). Vision Research Laboratory, University of California, Santa Barbara. Google Scholar
  4. Candes, E., Demanet, L., Donoho, D., & Ying, L. (2006). Fast discrete curvelet transforms. Multiscale Modeling and Simulation, 5(3), 861–899. MathSciNetMATHCrossRefGoogle Scholar
  5. Chaudhuri, B. B., & Sarkar, N. (1995). Texture segmentation using fractal dimension. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), 72–77. CrossRefGoogle Scholar
  6. Chen, L., Lu, G., & Zhang, D. S. (2004). Effects of different Gabor filter parameters on image retrieval by texture. In Proc. of IEEE 10th international conference on multi-media modelling, Australia, 2004 (pp. 273–278). CrossRefGoogle Scholar
  7. Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2), 5:1–60. CrossRefGoogle Scholar
  8. Daugman, J. G. (1985). Uncertainty relation for resolution in space, spatial frequency, and orientationoptimized by two-dimensional visual cortical filters. Journal of the Optical Society of America, 2(7), 1160–1169. CrossRefGoogle Scholar
  9. Daugman, J. G. (1988). Complete discrete 2-D Gabor transform by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(7), 1169–1179. MATHCrossRefGoogle Scholar
  10. Daugman, J. G. (1989). Entropy reduction and decorrelation in visual coding by oriented neural receptive fields. IEEE Transactions on Biomedical Engineering, 36(1), 107–114. CrossRefGoogle Scholar
  11. Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148–1161. CrossRefGoogle Scholar
  12. Deng, Y., & Manjunath, B. S. (2001). Unsupervised segmentation of color-texture regions in images and video. IEEE PAMI, 23(8), 800–810. CrossRefGoogle Scholar
  13. Do, M. N. (2001). Directional multiresolution image representations. Ph.D. thesis, EPFL. Google Scholar
  14. Do, M. N., & Vetterli, M. (2002). Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Transactions on Image Processing, 11(2), 146–158. MathSciNetCrossRefGoogle Scholar
  15. Do, M. N., & Vetterli, M. (2003). The finite ridgelet transform for image representation. IEEE Transactions on Image Processing, 12(1), 16–28. MathSciNetCrossRefGoogle Scholar
  16. Duygulu, P., Barnard, K., de Freitas, N., & Forsyth, D. (2002). Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In Proc. of the 7th European conf. on computer vision (pp. 97–112). Google Scholar
  17. Ferecatu, M., & Boujemaa, N. (2007). Interactive remote-sensing image retrieval using active relevance feedback. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 818–826. CrossRefGoogle Scholar
  18. Hervé, N., & Boujemaa, N. (2007). Image annotation: which approach for realistic databases? In Proc. of the 6th ACM international conf. on image and video retrieval, Amsterdam, Netherlands (pp. 70–177). Google Scholar
  19. Howarth, P., & Ruger, S. (2004). Evaluation of texture features for content-based image retrieval. Lecture Notes, 3115, 326–334. Google Scholar
  20. Huang, J., Kumar, S. R., Mitra, M., Zhu, W.-J., & Zabih, R. (1997). Image indexing using color correlograms. In Proc. of IEEE international conf. on computer vision and pattern recognition, San Juan, Puerto Rico, 17–19 June 1997 (pp. 762–768). Google Scholar
  21. Inoue, M. (2004). On the need for annotation-based image retrieval. In Proc. of SIGIR workshop on information retrieval in context (IRiX04), Sheffield, UK, 29th July (pp. 44–46). Google Scholar
  22. Islam, M., Zhang, D., & Lu, G. (2008). Automatic categorization of image regions using dominant color based vector quantization. In Proc. of digital image computing: techniques and applications (DICTA08), Canberra, Australia, 1–3 December (pp. 191–198). CrossRefGoogle Scholar
  23. Islam, M. M., Zhang, D., & Lu, G. (2009). Region based color image retrieval using curvelet transform. In Proc. of the 9th Asian conference on computer vision (ACCV2009), Xian, China, Sept. 23–27. Google Scholar
  24. Jeon, J., Lavrenko, V., & Manmatha, R. (2003). Automatic image annotation and retrieval using cross-media relevance models. In Proc. of the 26th annual international ACM SIGIR conference on research and development in information retrieval (pp. 119–126). Google Scholar
  25. Joutel, G., Eglin, V., Bres, S., & Emptoz, H. (2007a). Curvelets based feature extraction of handwritten shapes for ancient manuscripts classification. In SPIE: Vol. 6500. Proc. of SPIE-IS&T electronic imaging (65000D). Google Scholar
  26. Joutel, G., Eglin, V., Bres, S., & Emptoz, H. (2007b). Curvelets based queries for CBIR application in handwriting collections. In Ninth international conference on document analysis and recognition (ICDAR 2007) (pp. 649–653). Google Scholar
  27. Kokare, M., Biswas, P. K., & Chatterji, B. N. (2006). Rotation-invariant texture image retrieval using rotated complex wavelet filters. IEEE Transactions on Systems, Man and Cybernetics. Part B, 36(6), 1273–1282. CrossRefGoogle Scholar
  28. Lew, M. S., Sebe, N., Djeraba, C., & Jain, R. (2006). Content-based multimedia information retrieval: state of the art and challenges. ACM Transactions on Multimedia Computing Communications and Applications, 2(1), 1–19. CrossRefGoogle Scholar
  29. Li, S. Z., Chan, K. L., & Wang, C. (2000). Performance evaluation of the nearest feature line method in image classification and retrieval. IEEE PAMI, 22(11), 1335–1339. CrossRefGoogle Scholar
  30. Liu, F., & Picard, R. W. (1996). Periodicity, directionality, and randomness: wold features for image modeling and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 722–733. CrossRefGoogle Scholar
  31. Liu, Y., Zhang, D. S., & Lu, G. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1), 262–282. MATHCrossRefGoogle Scholar
  32. Liu, Y., Zhang, D., & Lu, G. (2008). Region-based image retrieval with high-level semantics using decision tree learning. Pattern Recognition, 41(8), 2554–2570. MATHCrossRefGoogle Scholar
  33. Long, F., Zhang, H. J., & Feng, D. D. (2003). Fundamentals of content-based image retrieval. In D. Feng (Ed.), Multimedia information retrieval and management. Berlin: Springer. Google Scholar
  34. Lu, Z., Li, S., & Burkhardt, H. (2006). A content-based image retrieval scheme in JPEG compressed domain. International Journal of Innovative Computing, Information and Control, 2(4), 831–839. Google Scholar
  35. Lu, Y., Zhang, L., Tian, Q., & Ma, W.-Y. (2008). What are the high-level concepts with small semantic gaps? In Proc. of international conf. on computer vision and pattern recognition (CVPR08), 23–28 June 2008 (pp. 1–8). Google Scholar
  36. Ma, W. Y., & Manjunath, B. S. (1995). A comparison of wavelet transform features for texture image annotation. In Proc. of the IEEE international conference on image processing (ICIP), Washington, DC, Oct. 23–26 (Vol. 2, pp. 256–259). CrossRefGoogle Scholar
  37. Manjunath, B. S., & Ma, W. Y. (1996). Texture features for browsing and retrieval of large image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), 837–842. CrossRefGoogle Scholar
  38. Manjunath, B. S., Ohm, J., Vasudevan, V. V., & Yamada, A. (2001). Color and texture descriptors. IEEE Transactions CSVT, 11(6), 703–715. Google Scholar
  39. Manjunath, B. S., Salembier, P., & Sikora, T. (2002). Introduction to MPEG-7. New York: Wiley. Google Scholar
  40. Ng, C. R., Lu, G., & Zhang, D. (2005). A new approach to texture retrieval. In Proc. of IEEE international workshop on multimedia signal processing (MMSP05), Shanghai, China, Oct. 30 to Nov. 2. Google Scholar
  41. Ngo, C. W., Pong, T. C., & Chin, R. T. (2001). Exploiting image indexing techniques in DCT domain. Pattern Recognition, 34(9), 1841–1851. MATHCrossRefGoogle Scholar
  42. Niblack, W., et al. (1993). The QBIC project: querying image by content using color, texture and shape. Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 1908, 173–187. Google Scholar
  43. Semler, L., & Dettori, L. (2006). Curvelet-based texture classification of tissues in computed tomography. In Proc. of the IEEE international conference on image processing, 8–11 Oct. (pp. 2165–2168). Google Scholar
  44. Shekhar, R., & Chaudhuri, S. (2005). In Lecture notes in computer science: Vol. 3776. Use of contourlets for image retrieval (pp. 563–569). Google Scholar
  45. Starck, J., Candès, E. J., & Donoho, D. L. (2002). The curvelet transform for image denoising. IEEE Transactions on Image Processing, 11(6), 670–684. MathSciNetCrossRefGoogle Scholar
  46. Suematsu, N., Ishida, Y., Hayashi, A., & Kanbara, T. (2002). Region-based image retrieval using wavelet transform. In Proc. 15th international conf. on vision interface, May 2002. Google Scholar
  47. Sumana, I., Islam, M., Zhang, D., & Lu, G. (2008). Content based image retrieval using curvelet transform. In Proc. of IEEE international workshop on multimedia signal processing (MMSP08), Cairns, Australia, October 8–10 (pp. 11–16). Google Scholar
  48. Tamura, H., Mori, S., & Yamawaki, T. (1978). Texture features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8(6), 460–473. CrossRefGoogle Scholar
  49. Vasconcelos, N. (2007). From pixels to semantic spaces: advances in content-based image retrieval. IEEE Computer, 40(7), 20–26. CrossRefGoogle Scholar
  50. Vertan, C., & Boujemaa, N. (2000). Upgrading color distributions for image retrieval: Can we do better? In Proc. int. conf. VISUAL, Nov. 2000 (pp. 178–188). Google Scholar
  51. Wang, J. Z., Li, J., & Wiederhold, G. (2001). SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947–963. CrossRefGoogle Scholar
  52. Wang, C., Jing, F., Zhang, L., & Zhang, H. (2006). Image annotation refinement using random walk with restarts. In Proc. of the 14th ACM international conference on multimedia, Santa Barbara, CA, USA, Oct. 23–27 (pp. 647–650). CrossRefGoogle Scholar
  53. Wang, C., Jing, F., Zhang, L., & Zhang, H.-J. (2007). Content-based image annotation refinement. In Proc. of international conf. on computer vision and pattern recognition (CVPR07) (pp. 1–8). Google Scholar
  54. Wang, C., Zhang, L., & Zhang, H. (2008a). Learning to reduce the semantic gap in web image retrieval and annotation. In Proc. of the 31st annual international ACM SIGIR conf. on research and development in information retrieval (SIGIR08), Singapore, 20–24 July 2008 (pp. 355–362). Google Scholar
  55. Wang, C., Zhang, L., & Zhang, H. (2008b). Scalable Markov model-based image annotation. In Proc. of international conference on content-based image and video retrieval (CIVR08), Canada, 07–09 July 2008 (pp. 113–118). CrossRefGoogle Scholar
  56. Zhang, D., & Lu, G. (2000). Content-based image retrieval using Gabor texture features. In Proc. of first IEEE pacific-rim conference on multimedia (PCM00), Sydney, Australia, December 13–15 (pp. 1139–1142). Google Scholar
  57. Zhang, D., & Lu, G. (2003). Evaluation of similarity measurement for image retrieval. In Proc. of IEEE international conference on neural networks & signal processing (ICNNSP03), Nanjing, China, Dec. 14–17 (pp. 928–931). CrossRefGoogle Scholar
  58. Zhang, D., & Lu, G. (2004). Review of shape representation and description techniques. Pattern Recognition, 37(1), 1–19. MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Dengsheng Zhang
    • 1
  • M. Monirul Islam
    • 1
  • Guojun Lu
    • 1
  • Ishrat Jahan Sumana
    • 1
  1. 1.Gippsland School of Information TechnologyMonash UniversityChurchillAustralia

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