Local Texton Dissimilarity with Applications on Biomass Classification

Radu Tudor Ionescu, Andreea-Lavinia Popescu, Dan Popescu, Marius Popescu

2014

Abstract

Texture classification, texture synthesis, or similar tasks are an active topic in computer vision and pattern recognition. This paper aims to present a novel texture dissimilarity measure based on textons, namely the Local Texton Dissimilarity (LTD), inspired from (Dinu et al., 2012). Textons are represented as a set of features extracted from image patches. The proposed dissimilarity measure shows its application on biomass type identification. A new data set of biomass texture images is provided by this work, which is available at http://biomass.herokuapp.com. Images are separated into three classes, each one representing a type of biomass. The biomass type identification and quality assessment is of great importance when one in the biomass industry needs to produce another energy product, such as biofuel, for example. Two more experiments are conducted on popular texture classification data sets, namely Brodatz and UIUCTex. The proposed method benefits from a faster computational time compared to (Dinu et al., 2012) and a better accuracy when used for texture classification. The performance level of the machine learning methods based on LTD is comparable to the state of the art methods.

References

  1. Barnes, C., Goldman, D. B., Shechtman, E., and Finkelstein, A. (2011). The PatchMatch Randomized Matching Algorithm for Image Manipulation. Communications of the ACM, 54(11):103-110.
  2. Brodatz, P. (1966). Textures: a photographic album for artists and designers. Dover pictorial archives. Dover Publications, New York, USA.
  3. Dash, J., Mathur, A., Foody, G. M., Curran, P. J., Chipman, J. W., and Lillesand, T. M. (2007). Land cover classification using multi-temporal MERIS vegetation indices. IJRS, 28(6):1137-1159.
  4. Daugman, J. G. (1985). Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A, 2(7):1160-1169.
  5. Dinu, L. P., Ionescu, R., and Popescu, M. (2012). Local Patch Dissimilarity for Images. Proceedings of ICONIP, 7663:117-126.
  6. Efros, A. A. and Freeman, W. T. (2001). Image quilting for texture synthesis and transfer. Proceedings of SIGGRAPH 7801, pages 341-346.
  7. Falconer, K. (2003). Fractal geometry: mathematical foundations and applications. Wiley, 2 edition.
  8. Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, 3(6):610-621.
  9. Hastie, T. and Tibshirani, R. (2003). The Elements of Statistical Learning. Springer, corrected edition.
  10. Hoekman, D. H. and Quinnones, M. J. (2000). Land cover type and biomass classification using AirSAR data fro evaluation of monitoring scenarios in the Colombian Amazon. IEEE Transactions on Geoscience and Remote Sensing, 38:685-696.
  11. Kuse, M., Wang, Y.-F., Kalasannavar, V., Khan, M., and Rajpoot, N. (2011). Local isotropic phase symmetry measure for detection of beta cells and lymphocytes. Journal of Pathology Informatics, 2(2):2.
  12. Lazebnik, S., Schmid, C., and Ponce, J. (2005a). A Maximum Entropy Framework for Part-Based Texture and Object Recognition. Proceedings of ICCV, 1:832- 838.
  13. Lazebnik, S., Schmid, C., and Ponce, J. (2005b). A Sparse Texture Representation Using Local Affine Regions. PAMI, 27(8):1265-1278.
  14. Leung, T. and Malik, J. (2001). Representing and Recognizing the Visual Appearance of Materials using Threedimensional Textons. IJCV, 43(1):29-44.
  15. Lowe, D. G. (1999). Object Recognition from Local ScaleInvariant Features. Proceedings of ICCV, 2:1150- 1157.
  16. Popescu, A. L., Popescu, D., Ionescu, R. T., Angelescu, N., and Cojocaru, R. (2013). Efficient Fractal Method for Texture Classification. Proceedings of ICSCS, (Accepted).
  17. Varma, M. and Zisserman, A. (2005). A Statistical Approach to Texture Classification from Single Images. IJCV, 62(1-2):61-81.
  18. Wulder, M. A., White, J. C., Fournier, R. A., Luther, J. E., and Magnussen, S. (2008). Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS. Sensors, 8(1):529-560.
  19. Xie, J., Zhang, L., You, J., and Zhang, D. (2010). Texture classification via patch-based sparse texton learning. Proceedings of ICIP, pages 2737-2740.
  20. Zhang, J., Marszalek, M., Lazebnik, S., and Schmid, C. (2007). Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. IJCV, 73(2):213-238.
Download


Paper Citation


in Harvard Style

Ionescu R., Popescu A., Popescu D. and Popescu M. (2014). Local Texton Dissimilarity with Applications on Biomass Classification . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 593-600. DOI: 10.5220/0004740105930600


in Bibtex Style

@conference{visapp14,
author={Radu Tudor Ionescu and Andreea-Lavinia Popescu and Dan Popescu and Marius Popescu},
title={Local Texton Dissimilarity with Applications on Biomass Classification},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={593-600},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004740105930600},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Local Texton Dissimilarity with Applications on Biomass Classification
SN - 978-989-758-003-1
AU - Ionescu R.
AU - Popescu A.
AU - Popescu D.
AU - Popescu M.
PY - 2014
SP - 593
EP - 600
DO - 10.5220/0004740105930600