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A multiscale approach to texture-based image retrieval

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Abstract

This paper presents research on a robust technique for texture-based image retrieval in multimedia museum collections. The aim is to be able to use a query image patch containing a single texture to retrieve images containing an area with similar texture to that in the query. The feature extractor used to build the feature vectors is based on an improved version of the discrete wavelet frames (DWF), proposed elsewhere. In order to utilise the feature extractor on real scene image datasets, a block-oriented decomposition technique, termed the multiscale sub-image matching method, is presented. The multiscale method, together with the DWF, provide an efficient content-based retrieval technique without the need for segmentation. The algorithms are tested on a range of databases of texture images as well as on real museum image collections. Promising results are reported.

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References

  1. Rui Y, Huang TS (1999) Image retrieval: current techniques, promising directions, and open issues. J Visual Comm Image Rep 10:39–62

    Article  Google Scholar 

  2. Hlaoui A, Sun H-J, Wang S-R (2002) Image retrieval using fuzzy segmentation and a graph matching technique. In: Proceedings of the international conference on machine learning and cybernetics, pp 1987–1992

  3. Liu Y, Zhou X (2004) Automatic texture segmentation for texture-based image retrieval. In: Proceedings of the international conference on multimedia modelling, pp 285–290

  4. Ko B, Peng J, Byun H (2001) Region-based image retrieval using probabilistic feature relevance learning. Pattern Anal Appl 4:174–184

    Article  MATH  MathSciNet  Google Scholar 

  5. Choras RS, Andrysiak T, Choras M (2007) Integrated color, texture and shape information for content-based image retrieval. Pattern Anal Appl. doi: 10.1007/s 10044-007-0071-0(online first)

  6. Rubner Y, Tomasi C (1999) Texture-based image retrieval without segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1018–1024

  7. Chan S, Martinez K, Lewis P, Lahanier C, Stevenson J (2001) Handling sub-image queries in content-based retrieval of high resolution art images. In: Proceedings of the international conference in cultural heritage and technologies, pp 157–163

  8. Fauzi MFA (2004) Content-based image retrieval of museum images. PhD Thesis, University of Southampton

  9. Pun C-M, Lee M-C (2002) Rotation invariant texture feature for content based image retrieval. In: Proceedings of the IEEE international conference on multimedia and expo, pp 173–176

  10. Muneeswaran K, Ganesan L, Arumugam S, Soundar KR (2005) Texture classification with combined rotation and scale invariant wavelet features. Pattern Recognit 38:1495–1506

    Article  MATH  Google Scholar 

  11. Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18:837–842

    Article  Google Scholar 

  12. Natsev A, Rastogi R, Shim K (1999) WALRUS: a similarity retrieval algorithm for image databases. In: Proceedings of ACM SIGMOD international conference on management of data, pp 395–406

  13. Smith JR, Chang S-F (1994) Quad-tree segmentation for texture based image query. In: Proceedings of the ACM multimedia conference, pp 279–286

  14. Guo J, Zhang A (1997) Image decomposition and representation in large image database systems. J Visual Comm Image Rep 8:167–181

    Article  Google Scholar 

  15. Graps A (1995) An introduction to wavelets. IEEE Comput Sci Eng 2:50–61

    Article  Google Scholar 

  16. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693

    Article  MATH  Google Scholar 

  17. Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4:1549–1560

    Article  Google Scholar 

  18. Chang T, Kuo CCJ (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2:429–441

    Article  Google Scholar 

  19. Fauzi MFA, Lewis PH (2006) Automatic texture segmentation for content-based image retrieval application. Pattern Anal Appl 9:307–323

    Article  MathSciNet  Google Scholar 

  20. Brodatz P (1966) Textures: a photographic album for artists & designers. Dover, New York

    Google Scholar 

  21. Picard R et al (1995) Vision Texture 1.0. MIT Media Lab, at http://www-white.media.mit.edu/vismod/imagery/VisionTexture/vistex.html

  22. Artiste Project. At http://www.artisteweb.org

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Acknowledgments

The authors are grateful to the Faculty of Engineering, Multimedia University, Malaysia and the School of Electronics and Computer Science at the University of Southampton, UK for financial support. We are also grateful to the EU for their support under grant number IST_1999_11978 (The Artiste Project), and to our collaborators, the Victoria and Albert Museum (London, UK), the National Gallery (London, UK) and the Research and Restoration Centre for the Museum of France (Paris, France) for use of their images.

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Authors and Affiliations

  1. Faculty of Engineering, Multimedia University, Jalan Multimedia, 63100, Cyberjaya, Selangor, Malaysia

    Mohammad Faizal Ahmad Fauzi

  2. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Paul H. Lewis

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  1. Mohammad Faizal Ahmad Fauzi
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  2. Paul H. Lewis
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Correspondence to Mohammad Faizal Ahmad Fauzi.

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Fauzi, M.F.A., Lewis, P.H. A multiscale approach to texture-based image retrieval. Pattern Anal Applic 11, 141–157 (2008). https://doi.org/10.1007/s10044-007-0085-7

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  • DOI: https://doi.org/10.1007/s10044-007-0085-7

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