Skip to main content
SpringerLink
Log in

Reverse engineering the way humans rank textures

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

We argue that in order to understand which features are used by humans to group textures, one must start by computing thousands of features of diverse nature, and select from those features those that allow the reproduction of perceptual groups or perceptual ranking created by humans. We use the Trace transform to produce such features here. We compare these features with those produced from the co-occurrence matrix and its variations. We show that when one is not interested in reproducing human behaviour, the elements of the co-occurrence matrix used as features perform best in terms of texture classification accuracy. However, these features cannot be “trained” or “selected” to imitate human ranking, while the features produced from the Trace transform can. We attribute this to the diverse nature of the features computed from the Trace transform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Each subject created their own perceptual groups. They were in large agreement between them. The final grouping was created by taking into consideration the majority opinion. Finally, the sets created were shown back to the subjects who agreed with them.

References

  1. Abbadeni N, Ziou D, Shengrui W (2000) Autocovariance-based perceptual textural features corresponding to human visual perception. In: Proceedings of the 15th international conference on pattern recognition, vol 3, pp 901–904, 3–7 September 2000

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

    Article  Google Scholar 

  3. Cohen FS, Fan Z, Patel MA (1991) Classification of rotated and scaled textured images using Gaussian Markov random field models. IEEE Trans Pattern Anal Mach Intell 13(2):192–202

    Article  Google Scholar 

  4. Haddon JF, Boyce JF (1992) Texture segmentation and region classification by orthogonal decomposition of co-occurrence matrices. In: Proceedings of the 11th international conference on pattern recognition, conference a: computer vision and applications, vol 1, pp 692–694

  5. Haddon JF, Boyce JF (1994) Texture classification of segmented regions of FLIR images using neural networks. In: IEEE Proceeding of the 1st international conference on image processing, pp 660–664

  6. Healey CG, Enns JT (1998) Building perceptual textures to visualize multidimensional datasets. In: Proceedings of Visualization, pp 111–118, 18–23 Octobter 1998

  7. Haley GM, Manjunath BS (1999) Rotation-invariant texture classification using a complete space-frequency model. IEEE Trans Image Process 8(2):255–269

    Article  Google Scholar 

  8. Haralick RM (1986) Handbook of pattern recognition and image processing. In: Young TY, Fu KS (eds) Chapter statistical image texture analysis. Academic, New York, pp 247–279

  9. Hoogs A, Collins R, Kaucic R (2002) Classification of 3D macro texture using perceptual observables. In: Proceedings of the 16th international conference on pattern recognition, vol 4, pp 113–117

  10. Iqbal Q, Aggarwall JK (1990) Applying perceptual grouping to content-based image retrieval: building images. IEEE Comput Soc Conf Comput Vis Pattern Recogn 1:42–48

    Google Scholar 

  11. Kadyrov A, Petrou M (1998) Application of the Trace transform to change detection. In: Singh S (ed) International conference on advances in pattern recognition. Plymouth, UK, November 23–25 1998, pp 55–62

  12. Kadyrov A, Petrou M (2001) The trace transform and its applications. IEEE Trans Pattern Anal Mach Intell 23:811–828

    Article  Google Scholar 

  13. Kadyrov A, Talebpour A, Petrou M (2002) Texture classification with thousands of features. In: Rosin PL, Marshall D (eds) British Machine Vision Conference, ISBN 1 901725 19 7, vol 2, pp 656–665, 2–5 September, 2002, Cardiff

  14. Kovalev V, Petrou M (1996) Multidimensional co-occurrence matrices for object recognition and matching. Graph Models Image Process 58(3):187–197

    Article  Google Scholar 

  15. Kovalev V, Volmer S (1998) Color co-occurrence descriptors for querying-by-example. In: Proceedings of the multimedia modeling, pp 32–38, 12–15 October 1998

  16. Liu F, Picard R (1996) Periodicity, directionality and randomness: wold features for image modelling and retrieval. IEEE Trans Pattern Anal Mach Intell 18:722–733

    Article  Google Scholar 

  17. Long H, Leow WK (2002) A hybrid model for invariant and perceptual texture mapping. In: Proceedings of the 16th international conference on pattern recognition, vol 1, pp 135–138

  18. 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 

  19. Orrite C, Roy A, Alcolea (1999) Surface segmentation based on perceptual grouping. In: Proceedings of the international conference on image analysis and processing, pp 328–333, 27–29 September 1999

  20. Palmer SE (1983) The psychology of perceptual organization: a transformational approach. In: Beck J, Hope B, Rosenfeld A (eds) Human and machine vision, pp 269–339

  21. Payne JS, Heppelwhite L, Stonham TJ (1999) Perceptually based metrics for the evaluation of textural image retrieval methods. IEEE Int Conf Multimed Comput Syst 2:793–797

    Article  Google Scholar 

  22. Petrou A, Kadyrov A (2004) Affine invariant features from the trace transform. IEEE Trans Pattern Anal Mach Intell 26(1):30–44

    Article  Google Scholar 

  23. Rao R, Lohse GL (1993) Towards a texture naming system: identifying relevant dimensions of texture. In: Proceedings of the 4th conference on visualisation 1993, San Jose, CA, October 1993, pp 220–227

  24. Reed T, Du Buf JMH (1993) Review of recent texture segmentation feature extraction techniques. Graph Image Process (CVGIP): Image Understand Comput Vis 57(3):359–372

    Article  Google Scholar 

  25. Sayeed A, Petrou M, Spyrou N, Kadyrov A, Spinks T (2002) Diagnostic features of Alzheimer’s disease extracted from PET sinograms. Phys Med Biol 47:137–148

    Article  Google Scholar 

  26. Talebpour A (2004) Texture analysis using the trace transform. PhD Thesis, University of Surrey

  27. Teuner A, Pichler O, Santos Conde JE, Hosticka BJ (1997) Orientation- and scale-invariant recognition of textures in multi-object scenes. In: Proceedings of the international conference on image processing, vol 3, pp 174–177, 26–29 October 1997

  28. Unser M (1986) Sum and difference histograms for texture classification. IEEE Trans Pattern Anal Mach Intell 8:118–125

    Article  Google Scholar 

  29. Yow KC, Cipolla R (1996) A probabilistic framework for perceptual grouping of features for human face detection. In: Proceedings of the second international conference on automatic face and gesture recognition. pp 16–21, 14–16 October 1996

  30. http://www.ux.his.no/tranden/brodatz.htm. Brodatz Album

Download references

Acknowledgments

This project was supported by the RCUK Basic Technology grant “Reverse Engineering the human vision system”.

Author information

Authors and Affiliations

  1. Communications and Signal processing Group, Electrical and Electronic Engineering, Imperial College, London, SW7 2AZ, UK

    Maria Petrou & Alexander Kadyrov

  2. Centre for Vision, Speech and Signal Processing, Survey University, Guildford, GU2 7XH, UK

    Alireza Talebpour

Authors
  1. Maria Petrou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Talebpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Kadyrov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria Petrou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Petrou, M., Talebpour, A. & Kadyrov, A. Reverse engineering the way humans rank textures. Pattern Anal Applic 10, 101–114 (2007). https://doi.org/10.1007/s10044-006-0054-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-006-0054-6

Keywords

Search

Navigation