Complexity Aspects of Image Classification
- Cite this paper as:
- Albrecht A.A. (2008) Complexity Aspects of Image Classification. In: Gao X., Müller H., Loomes M.J., Comley R., Luo S. (eds) Medical Imaging and Informatics. Lecture Notes in Computer Science, vol 4987. Springer, Berlin, Heidelberg
Feature selection and parameter settings for classifiers are both important issues in computer-assisted medical diagnosis. In the present paper, we highlight some of the complexity problems posed by both tasks. For the feature selection problem we propose a search-based procedure with a proven time bound for the convergence to optimum solutions. Interestingly, the time bound differs from fixed-parameter tractable algorithms by an instance-specific factor only. The stochastic search method has been utilized in the context of micro array data classification. For the classification of medical images we propose a generic upper bound for the size of classifiers that basically depends on the number of training samples only. The evaluation on a number of benchmark problems produced a close correspondence to the size of classifiers with best generalization results reported in the literature.
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