Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we propose an optimal image sampling model based on the general scheme of Bootstrap sampling to get rid of dependence effect of pixels in real images, and to reduce the segmentation time. Given an original image, we randomly select a small representative set of pixels. Then, a stochastic model based on the finite normal mixture distribution identification is used for image segmentation. A local unsupervised segmentation method based on Expectation-Maximization (EM) family algorithms is then used for parameter estimation, and the Maximum Likelihood Classification (MLC) is adopted for pixel classlfication. We finally compare our Bootstrap approach to the classical EM family algorithms that make a determinist sampling pixel after pixel for parameter estimation. The results we obtain show that our Bootstrap Sample Selection method gives better results than the classical one both in the quality of the segmented image and the computating time.