Image retrieval measures based on illumination invariant textural MRF features

ABSTRACT

Content-based image retrieval (CBIR) systems, target database images using feature similarities with respect to the query. We introduce fast and robust image retrieval measures that utilise novel illumination invariant features extracted from three different Markov random field (MRF) based texture representations. These measures allow retrieving images with similar scenes comprising colour textured objects viewed with different illumination brightness or spectrum.

The proposed illumination insensitive measures are compared favourably with the most frequently used features like the Local Binary Patterns, steerable pyramid and Gabor textural features, respectively. The superiority of these new illumination invariant measures and their robustness to added noise are empirically demonstrated in the illumination invariant recognition of textures from the Outex database.

REFERENCES

• 1: A. Bovik. Analysis of multichannel narrow-band filters for image texture segmentation. IEEE Trans. on Signal Processing, 39(9):2025--2043, 1991.
• 2: H. F. Chen, P. N. Belhumeur, and D. W. Jacobs. In search of illumination invariants. In CVPR, pages I: 254--261, 2000.
• 3: O. Drbohlav and M. Chantler. Illumination-invariant texture classification using single training images. In M. Chantler and O. Drbohlav, editors, Texture 2005., pages 31--36, Edinburgh, October 2005. Heriot-Watt University.
• 4: G. D. Finlayson. Colour object recognition. Master's thesis, Simon Fraser University, 1992.
• 5: Graham David Finlayson , Brian Funt, Coefficient color constancy, Simon Fraser University, Burnaby, BC, Canada, 1995
• 6: Graham Finlayson , Ruixia Xu, Illuminant and gamma comprehensive normalisation in log RGB space, Pattern Recognition Letters, v.24 n.11, p.1679-1690, July 2003 [doi>10.1016/S0167-8655(02)00324-0]
• 7: Jan-Mark Geusebroek , Rein van den Boomgaard , Arnold W. M. Smeulders , Theo Gevers, Color constancy from physical principles, Pattern Recognition Letters, v.24 n.11, p.1653-1662, July 2003 [doi>10.1016/S0167-8655(02)00322-7]
• 8: Jan-Mark Geusebroek , Arnold W. M. Smeulders, A Six-Stimulus Theory for Stochastic Texture, International Journal of Computer Vision, v.62 n.1-2, p.7-16, April-May 2005 [doi>10.1007/s11263-005-4632-7]
• 9: T. Gevers and A. W. M. Smeulders. Color constant ratio gradients for image segmentation and similarity of texture objects. In CVPR, pages 18--25. IEEE Computer Society, 2001.
• 10: M. Haindl and V. Havlíček. Prototype Implementation of the Texture Analysis Objects. Technical Report 1939, ÚTIA AV ČR, Praha, 1997.
• 11: Michal Haindl , Stanislava Simberová, A Multi-Model Image Line Reconstruction, Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns, p.735-740, September 06-08, 1995
• 12: G. Healey , Lizhi Wang, The illumination-invariant recognition of color texture, Proceedings of the Fifth International Conference on Computer Vision, p.128, June 20-23, 1995
• 13: Minh A. Hoang , Jan-Mark Geusebroek , Arnold W. M. Smeulders, Color texture measurement and segmentation, Signal Processing, v.85 n.2, p.265-275, February 2005 [doi>10.1016/j.sigpro.2004.10.009]
• 14: Pei-hsiu Suen , Glenn Healey, The Analysis and Recognition of Real-World Textures in Three Dimensions, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.5, p.491-503, May 2000 [doi>10.1109/34.857005]
• 15: D. W. Jacobs , P. N. Belhumeur , R. Basri, Comparing Images under Variable Illumination, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.610, June 23-25, 1998
• 16: A. Jain and G. Healey. A multiscale representation including opponent colour features for texture recognition. IEEE Trans. on Image Processing, 7(1):125--128, January 1998.
• 17: Topi Mäenpää , Matti Pietikäinen , Jaakko Viertola, Separating Color and Pattern Information for Color Texture Discrimination, Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1, p.10668, August 11-15, 2002
• 18: B. S. Manjunath , W. Y. Ma, Texture Features for Browsing and Retrieval of Image Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.18 n.8, p.837-842, August 1996 [doi>10.1109/34.531803]
• 19: Timo Ojala , Topi Mäenpää , Matti Pietikäinen , Jaakko Viertola , Juha Kyllönen , Sami Huovinen, Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms, Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1, p.10701, August 11-15, 2002
• 20: Timo Ojala , Matti Pietikäinen , Topi Mäenpää, Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.24 n.7, p.971-987, July 2002 [doi>10.1109/TPAMI.2002.1017623]
• 21: M. Pietikainen, T. Maenpaa, and J. Viertola. Color texture classification with color histograms and local binary patterns. In Workshop on Texture Analysis in Machine Vision, pages 109--112, 2002.
• 22: Trygve Randen , John Hå Husøy, Filtering for Texture Classification: A Comparative Study, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.21 n.4, p.291-310, April 1999 [doi>10.1109/34.761261]
• 23: E. Simoncelli and J. Portilla. Texture characterization via joint statistics of wavelet coefficient magnitudes. In Fifth IEEE Int'l Conf on Image Proc, volume I, Chicago, 4-7 1998. IEEE Computer Society.
• 24: Arnold W. M. Smeulders , Marcel Worring , Simone Santini , Amarnath Gupta , Ramesh Jain, Content-Based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.12, p.1349-1380, December 2000 [doi>10.1109/34.895972]
• 25: Manik Varma , Andrew Zisserman, A Statistical Approach to Texture Classification from Single Images, International Journal of Computer Vision, v.62 n.1-2, p.61-81, April-May 2005 [doi>10.1007/s11263-005-4635-4]
• 26: Joost van de Weijer , Theo Gevers , Jan-Mark Geusebroek, Edge and Corner Detection by Photometric Quasi-Invariants, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.27 n.4, p.625-630, April 2005 [doi>10.1109/TPAMI.2005.75]
• 27: Jie Yang , Mohammed Al-Rawi, Illumination invariant recognition of three-dimensional texture in color images, Journal of Computer Science and Technology, v.20 n.3, p.378-388, May 2005 [doi>10.1007/s11390-005-0378-5]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.7 Feature Measurement
          Subjects: Invariants; Texture
  I.5 PATTERN RECOGNITION
      I.5.1 Models
          Subjects: Statistical

General Terms:
Algorithms, Measurement, Theory

Keywords:
Markov random fields (MRF), content-based image retrieval (CBIR), illumination invariants