Using different algorithms to segment different images is a quite straightforward strategy for automated image segmentation. But the difficulty of the optimal algorithm selection has prevented it from being used for many years. In this paper, a framework of algorithm selection system is proposed to achieve automated image segmentation. Off-line learning scheme is adopted to make use of interactive segmentation evaluation. During training, both the performance ranks of candidate algorithms on every image and image features are used to train a predictor. Then, the performance ranks of all candidates will be predicted according to image features. Finally, the algorithm with the highest rank will be regarded as optimal and applied to the image. A simulation system is constructed to select optimal segmentation algorithm from four candidates for synthetic images. In this system, histogram is used as image feature, the number of misclassified pixels and computation expenses are used to facilitate interactive segmentation evaluation, and Principle Components Analysis (PCA) and Support Vector Machine (SVM) are used to construct the predictor. The system is tested on 9000 images by comparing with the manual selection. The best algorithms are selected for 84.90% of cases. If the second best algorithm is also regarded as acceptable, more than 97.5% of images can be properly segmented. The satisfied results demonstrate that this study has provided a promising approach to automated image segmentation.
|Cite as: Yong, X., Feng, D.D. and Rongchun, Z. (2004). Optimal Selection of Image Segmentation Algorithms Based on Performance Prediction. In Proc. 2003 Pan-Sydney Area Workshop on Visual Information Processing (VIP2003), Sydney, Australia. CRPIT, 36. Piccardi, M., Hintz, T., He, S., Huang, M. L. and Feng, D. D., Eds. ACS. 105-108. |
(local if available)