Geostatistics for Context-Aware Image Classification
Context information is fundamental for image understanding. Many algorithms add context information by including semantic relations among objects such as neighboring tendencies, relative sizes and positions. To achieve context inclusion, popular context-aware classification methods rely on probabilistic graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF). However, recent studies showed that MRF/CRF approaches do not perform better than a simple smoothing on the labeling results.
The need for more context awareness has motivated the use of different methods where the semantic relations between objects are further enforced. With this, we found that on particular application scenarios where some specific assumptions can be made, the use of context relationships is greatly more effective.
We propose a new method, called GeoSim, to compute the labels of mosaic images with context label agreement. Our method trains a transition probability model to enforce properties such as class size and proportions. The method draws inspiration from Geostatistics, usually used to model spatial uncertainties. We tested the proposed method in two different ocean seabed classification context, obtaining state-of-art results.
KeywordsContext adding Underwater vision Geostatistics Conditional random fields
The authors would like to thank to the Brazilian National Agency of Petroleum, Natural Gas and Biofuels(ANP), to the Funding Authority for Studies and Projects(FINEP) and to Ministry of Science and Technology (MCT) for their financial support through the Human Resources Program of ANP to the Petroleum and Gas Sector - PRH-ANP/MCT.
This paper is also a contribution of the Brazilian National Institute of Science and Technology - INCT-Mar COI funded by CNPq Grant Number 610012/2011-8.
Additional support was granted by the Spanish National Project OMNIUS (CTM2013-46718-R), and the Generalitat de Catalunya through the TECNIOspring program (TECSPR14-1-0050) to N. Gracias.
- 1.Agterberg, F.: Mathematical geology. In: General Geology. Encyclopedia of Earth Science, pp. 573–582. Springer, US (1988). http://dx.doi.org/10.1007/0-387-30844-X_76
- 3.Beattie, C., Mills, B., Mayo, V.: Development drilling of the tawila field, yemen, based on three-dimensional reservoir modeling and simulation. In: SPE Annual Technical Conference, pp. 715–725 (1998)Google Scholar
- 11.Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFS with Gaussian edge potentials. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 109–117. Curran Associates, Inc (2011)Google Scholar
- 13.Lucchi, A., Li, Y., Boix, X., Smith, K., Fua, P.: Are spatial and global constraints really necessary for segmentation? In: IEEE International Conference on Computer Vision (ICCV), pp. 9–16. IEEE (2011)Google Scholar
- 17.Tu, Z.: Auto-context and its application to high-level vision tasks. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
- 18.Yedidia, J.S., Freeman, W.T., Weiss, Y., et al.: Generalized belief propagation. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems, vol. 13, pp. 689–695. MIT Press (2001)Google Scholar