Identification of Diatoms by Grid Graph Matching
Diatoms are unicellular algae found in water and other places wherever there is humidity and enough light for photo synthesis. In this paper a graph matching based identification approach for the retrieval of diatoms from an image database is presented. The retrieval is based on the matching of labeled grid graphs carrying texture information of the underlying diatom. A grid graph is a regular, rectangular arrangement of nodes overlaid on an image. Each node of the graph is labeled with texture features describing a rectangular sub-region of the object. Properties of gray level co-occurrence matrices as well as of Gabor wavelets are used as texture features. The method has been evaluated on a diatom database holding images of 188 different diatoms belonging to 38 classes. For the identification of these diatoms recognition rates of more than 90 percent were obtained.
KeywordsRecognition Rate Image Database Query Image Graph Match Gabor Wavelet
- 1.Automatic Diatom Identification And Classification. Project home page: http://www.ualg.pt/adiac/.
- 3.H. Bunke. Recent developments in graph matching. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR’ 00), volume 2, pages 117–124, Barcelona, Spain, September 3–8 2000.Google Scholar
- 4.S. Fischer, M. Binkert, and H. Bunke. Feature based retrieval of diatoms in an image database using decision trees. In Proceedings of the 2nd International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2000), pages67–72, Baden-Baden, Germany, August 2000.Google Scholar
- 5.S. Fischer, M. Binkert, and H. Bunke. Symmetry based indexing of diatoms in an image database. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR’ 00), volume 2, pages 899–902, Barcelona, Spain, September 3–8 2000.Google Scholar
- 6.K. Gilomen. Texture based identification of diatoms (in German). Master’s thesis, University of Bern, 2001.Google Scholar
- 7.R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, pages 610–621, 1973.Google Scholar
- 11.J. L. Pappas and E. F. Stoermer. Multidimensional analysis of diatom morphological phenotypic variation and relation to niche. Ecoscience, 2:357–367, 1995.Google Scholar
- 12.I. Pitas. Digital image processing algorithms. Prentice Hall, London, 1993.Google Scholar
- 14.E. F. Stoermer and J. P. Smol, editors. The Diatoms: Applications for the Environmental and Earth Science. Cambridge University Press, 1999.Google Scholar
- 16.M. Tuceryan and A. K. Jain. Texture analysis. In C. H. Chen, L. F. Pau, and P. S. P. Wang, editors, The Handbook of Pattern Recognition and Computer Vision, pages 207–248. World Scientific Publishing Co, 2 edition, 1998.Google Scholar
- 17.M. Wilkinson, J. Roerdink, S. Droop, and M. Bayer. Diatom contour analysis using morphological curvature scale spaces. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR’ 00), pages 656–659, Barcelona, Spain, September 3–7 2000.Google Scholar