Pattern Recognition Combining De-noising and Linear Discriminant Analysis within a Real World Application
Computer aided systems based on image analysis have become popular in zoological systematics in the recent years. For insects in particular, the difficult taxonomy and the lack of experts greatly hampers studies on conservation and ecology. This problem was emphasized at the UN Conference of Environment, Rio 1992, leading to a directive to intensify efforts to develop automated identification systems for pollinating insects. We have developed a system for the automated identification of bee species which employs image analysis to classify bee forewings. Using the knowledge of a zoological expert to create learning sets of images together with labels indicating the species membership, we have formulated this problem in the framework of supervised learning. While the image analysis process is documented in , we describe in this paper a new model for classification that consists of a combination of Linear Discriminant Analysis with a de-noising technique based on a nonlinear generalization of principal component analysis, called Kernel PCA. This model combines the property of visualization provided by Linear Discriminant Analysis with powerful feature extraction and leads to significantly improved classification performance.
KeywordsLinear Discriminant Analysis Radial Basis Function Kernel Fisher Linear Discriminant Nonlinear Generalization Call Support Vector
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- 1.Duda R.O., Hart P.E. (1973) Pattern Classification and Scene Analysis, Wiley & SonsGoogle Scholar
- 2.Roth V., Schröder S., Cremers A.B., Drescher W., Steinhage V., Wittmann D. (1999) Computergestützte Klassikation von Wildbienen mit Methoden der Bildanalyse, Wissenschaftliche Berichte FZKA 6252, ISSN 0947-8620Google Scholar
- 4.Schölkopf B., Mika S., Smola A., Rätsch G., Müller K.R. (1998) Kernel PCA Pattern Reconstruction via Approximate Pre-Images, In: Niklasson L., Boden M. and Ziemke T. (eds.), Proceedings of the 8th International Conference on Artificial Neural Networks, Springer, Perspectives in Neural Computing.Google Scholar
- 5.Steinhage V., Kastenholz B., Schröder S., Drescher W (1997), A Hierarchical Approach to Classify Solitary Bees Based on Image Analysis, In: Mustererkennung 1997, 19. DAGM-Symposium, Braunschweig, Informatik aktuell, SpringerGoogle Scholar
- 6.Vapnik V. (1998) Statistical Learning Theory, Wiley & SonsGoogle Scholar