Pavel Vacha
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.
- A. Bovik. Analysis of multichannel narrow-band filters for image texture segmentation. IEEE Trans. on Signal Processing, 39(9):2025--2043, 1991.Google Scholar
Digital Library
- H. F. Chen, P. N. Belhumeur, and D. W. Jacobs. In search of illumination invariants. In CVPR, pages I: 254--261, 2000.Google ScholarCross Ref
- 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.Google Scholar
- G. D. Finlayson. Colour object recognition. Master's thesis, Simon Fraser University, 1992.Google Scholar
- G. D. Finlayson. Coefficient color constancy. PhD thesis, Simon Fraser University, 1995. Google ScholarDigital Library
- G. Finlyason and R. Xu. Illuminant and gamma comprehensive normalisation in logrgb space. Patterm Recognition Letters, 24:1679--1690, 2002. Google ScholarDigital Library
- J.-M. Geusebroek, R. v. d. Boomgaard, A. W. Smeulders, and T. Gevers. Colour constancy from physical principes. Pattern Recognition Letters, 24:1653--1662, 2003. Google ScholarDigital Library
- J.-M. Geusebroek and A. W. Smeulders. A six-stimulus theory for stochastic texture. Int. Journal of Computer Vision, 62:7--16, 2005. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- M. Haindl and V. Havlíček. Prototype Implementation of the Texture Analysis Objects. Technical Report 1939, ÚTIA AV ČR, Praha, 1997.Google Scholar
- M. Haindl and S. Šimberová. Theory & Applications of Image Analysis, chapter A Multispectral Image Line Reconstruction Method, pages 306--315. World Scientific Publishing Co., Singapore, 1992. Google ScholarDigital Library
- G. Healey and L. Wang. The illumination-invariant recognition of texture in color texture. In ICCV, pages 128--133, 1995. Google ScholarDigital Library
- M. A. Hoang and A. W. Geausebroek, Jan-Mark Smeulders. Color texture measurement and segmentation. Signal Processing, 85:295--275, 2005. Google ScholarDigital Library
- P. Hsiu Suen and G. Healey. The analysis and reconstruction of real-world textures in three dimensions. IEEE Trans. on Pattern Anal. and Mach. Intell, 22(5):491--503, May 2000. Google ScholarDigital Library
- D. Jacobs, P. Belhumeur, and R. Basri. Comparing images under variable illumination. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2000, volume 1, pages 610--617. IEEE, IEEE, June 1998. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- T. Maenpaa, M. Pietikainen, and J. Viertola. Separating color and pattern information for color texture discrimination. In ICPR, pages I: 668--671, 2002. Google ScholarDigital Library
- B. S. Manjunath and W. Y. Ma. Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. and Mach. Intell, 18(8):837--842, August 1996. Google ScholarDigital Library
- T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen, and S. Huovinen. Outex- new framework for empirical evaluation of texture analysis algorithms. In 16th ICPR, pages 701--706, August 2002. Google ScholarDigital Library
- T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell, 24(7):971--987, 2002. Google ScholarDigital Library
- 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.Google Scholar
- T. Randen and J. H. Husøy. Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. and Mach. Intell, 21(4):291--310, April 1999. Google ScholarDigital Library
- 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.Google Scholar
- A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. and Mach. Intell, 22(12):1349--1380, 2000. Google ScholarDigital Library
- M. Varma and A. Zisserman. A statistical approach to texture classification from single images. Int. Journal of Computer Vision, 62(1--2):61--81, 2005. Google ScholarDigital Library
- J. Weijer, T. Gevers, and J. Geusebroek. Edge and corner detection by photometric quasi-invariants. IEEE Trans. Pattern Anal. and Mach. Intell, 27(4):625--630, 2005. Google ScholarDigital Library
- J. Yang and M. Al-Rawi. Illumination invariant recognition of three-dimensional texture in color images. J. Comput. Sci. Technol, 20(3):378--388, 2005. Google ScholarDigital Library
Index Terms
- Image retrieval measures based on illumination invariant textural MRF features
Recommendations
Demonstration of image retrieval based on illumination invariant textural MRF features
CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrievalContent-based image retrieval (CBIR) systems target database images using feature similarities with respect to the query. Our CBIR demonstration utilises novel illumination invariant features, which are extracted from Markov random field (MRF) based ...
Distinctive image features from illumination and scale invariant keypoints
AbstractThis paper proposes a novel local feature descriptor of the image, which is named iSIFT (illumination and Scale Invariant Feature Transform), based on SIFT (Scale Invariant Feature Transform) improved by LBP (Local Binary Pattern), in order to ...
Discriminating Structure and Random Texture Images with Binary Fourier Spectrum for Invariant Texture Retrieval
APCIP '09: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02Because of the diversity of the textures, pre-classification before the retrieval seems very necessary and important. We divided texture images into two categories: structure and random, based on binary Fourier spectrum and Shannon entropy as a pre-...
Comments