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In depth analysis and evaluation of saliency-based color image indexing methods using wavelet salient features

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Abstract

Nowadays, most of the research works in the area of image retrieval try to build an image signature by considering the image as a whole. In this paper, we proposed an alternative based on the detection of some salient points in the image. For this purpose, we propose a new efficient salient point detector based on a wavelet transform. The efficiency of our detector lies in the representation of the wavelet coefficients by a zerotree data structure and by a saliency formulation that does not favor any direction. Thus, the detected salient points are located on sharp region boundaries whatever their direction. From the detected salient points, we build a color/texture signature by using jointly the well-known color correlogram extended to salient features and rotated wavelet filter responses. Experimental results conducted by adopting a global salient approach and a local salient approach show the effectiveness of the proposed scheme.

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References

  1. Achard C, Bigorgne E, Devars J (2000) A sub-pixel and multispectral corner detector. In: Proceedings of the International Conference on Pattern Recognition

  2. Bigorgne E, Achard C, Devars J (2001) A local color descriptor for efficient scene-object recognition. In: Proceedings of 11\(^{th}\) International Conference on Image Analysis and Processing, pp 440–445

  3. Bres S, Jolion JM (1999) Detection of interest points for image indexation. In: S Verlag (ed) \(3^{rd}\) Int. Conf. on Visual Information Systems. Amsterdam, pp 427–434

  4. Carson C, Belongie S, Greespan H, Malik J (2002) Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans Pattern Anal Mach Intell 24(8):1026–1038

    Article  Google Scholar 

  5. Daugman JG (1988) Complete discrete 2D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech and Signal Processing 36(7):1169–1179

    Article  MATH  Google Scholar 

  6. Geusebroek JM, Van den Boomgrad R, Smeulders WM., Geerts H (2001) Color invariance. IEEE Trans Pattern Anal Mach Intell 23(12):1338–1350

    Article  Google Scholar 

  7. Gevers T, Smeulders AWM (1999) Color based object recognition. Pattern Recogn 32:453–464

    Article  Google Scholar 

  8. Gevers T, Smeulders WM (2000) PicToSeek: combining color and shape invariant features for image retrieval. IEEE Trans Image Process 9(1):102–119

    Article  Google Scholar 

  9. Gordon IE (1997) Theories of Visual Perception. Wiley, New York, 2nd ED

  10. Haralick RM (1979) Statistical and Structural Approaches to texture. In: Proceedings of the IEEE, pp 786–804

  11. Harris C, Stephens M (1988) A combined corner and edge detector. In: \(4^{th}\) Alvey Vision Conf, pp 147–151

  12. Huang J, Kumar SR, Mitra M, Zhu W, Zabih R (1999) Spatial color indexing and applications. Int J Comput Vis 35(3):245–268

    Article  Google Scholar 

  13. Huang J, Kumar SR, Mitra M, Zhu WJ (1997) Image indexing using color correlograms. In: Proc. of the 16\(^th\) IEEE Conf. on Computer Vision and Pattern Recognition, pp 762–768

  14. Huijsmans DP, Sebe N (2001) Extended performance graphs for cluster retrieval. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Vol. 1. Kauai (Hawaii), pp 26–31

  15. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259

    Article  Google Scholar 

  16. Jolion JM, Bres S (1999) Indexation d'images et buit de codage. TS Trait Signal 15(4):309–320

    Google Scholar 

  17. Kim ND, Udpa S (2000) Texture classification using rotated wavelet filters. IEEE Trans Syst Man Cybern, Part A, Syst Humans 6(6):847–852

    MATH  Google Scholar 

  18. Laurent C, Laurent N, Visani M (2003) Color image retrieval based on wavelet salient features detection. In: Third International Workshop on Content-Based Multimedia Indexing. Rennes (France), pp 327–334

  19. Loupias E, Sebe N, Bres S, Jolion JM (2000) Wavelet-Based Salient Points for Image Retrieval. In: IEEE Int. Conf. on Image Processing. Vancouver

  20. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision. Corfu (Greece), pp 1150–1157

  21. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  MATH  Google Scholar 

  22. Mandal MK, Aboulnasr T, Panchanathan S (1996) Image indexing using moments and wavelets. IEEE Trans Consum Electron 42(3):557–565

    Article  Google Scholar 

  23. Marr D (1982) Vision. Freeman, San Francisco, California

  24. Mikolajczyk K (2002) Detection of local features invariant to affine transformations. Ph.D. thesis, Institut National Polytechnique de Grenoble, Grenoble (France)

  25. Mikolajczyk K, Schmid C (2001) Indexing based on scale invariant interest points. In: Proceedings of the International Conference on Computer Vision, pp 525–531

  26. Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector. In: Proceedings of the European Conference on Computer Vision, pp 128–142

  27. Mikolajczyk K, Schmid C (2003) A performance evaluation of local descriptors. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. Madison (USA)

  28. Montesinos P, Gouet V, Deriche R (1998) Differential invariants for color images. In: Proc. of the IAPR Conf. on Pattern Recognition. Brisbane

  29. Schmid C, Mohr R (1997) Local grayvalue invariants for image retrieval. IEEE Trans Pattern Anal Mach Intell 19(5):530–535

    Article  Google Scholar 

  30. Schmid C, Mohr R, Bauckhage C (2000) Evaluation of interest point detectors. Int J Comput Vis 37(2):151–172

    Article  MATH  Google Scholar 

  31. Sebe N, Lew MS (2001) Salient points for content-based retrieval. In: Proc. of the British Machine Vision Conference. Manchester-UK

  32. Sebe N, Lew MS (2003) Comparing salient point detectors. Pattern Recogn Lett 24:89–96

    Article  MATH  Google Scholar 

  33. Sebe N, Tian Q, Loupias E, Lew MS, Huang TS (2000) Color indexing using wavelet-based salient points. In: Proc. of the IEEE Workshop on Content-Based Access of Image and Video Libraries. Hilton Head, South California, pp 15–19

  34. Shapiro JM (1993) Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Process 41(12):3445–3462

    Article  MATH  Google Scholar 

  35. Shim S, Choi T (2002) Edge color histogram for image retrieval. In: IEEE International Conference on Image Processing, Vol. 3. Rochester, New York, pp 957–960.

  36. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380

    Article  Google Scholar 

  37. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32

    Article  Google Scholar 

  38. Tuceryan M, Jain AK (1993) Texture analysis. Handbook of Pattern Recognition and Computer Vision (42):235–276

  39. Wang JZ, Li J, Wiederhold G (2001) Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963

    Article  Google Scholar 

  40. Weschler H (1980) Texture analysis-a survey. Signal Processing 2(12):271–282

    Google Scholar 

  41. Wolf C, Jolion JM, Kropatsch W, Bischof H (2000) Content based image retrieval using interest points and texture features. In: Proc. of the Int. Conf. on Pattern Recognition, Vol. 4. Barcelona, pp 234–237

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Authors and Affiliations

  1. France Telecom R&D-DIH/HDM, 4, Rue du Clos Courtel, 35512, Cesson Sévigné Cedex, France

    Christophe Laurent, Nathalie Laurent, Mariette Maurizot & Thierry Dorval

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  1. Christophe Laurent
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  2. Nathalie Laurent
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  3. Mariette Maurizot
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  4. Thierry Dorval
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Correspondence to Christophe Laurent.

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Laurent, C., Laurent, N., Maurizot, M. et al. In depth analysis and evaluation of saliency-based color image indexing methods using wavelet salient features. Multimed Tools Appl 31, 73–94 (2006). https://doi.org/10.1007/s11042-006-0036-0

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