Multi-scale bi-domain Bayesian classifier designed for infrared image segmentation

Abstract

An extended Bayesian classifier, which is able to fuse information in original image and in its wavelet domain, is designed for infrared image segmentation. The algorithm begins with a re-sampling process over the original image and a wavelet transformation of the original image. Then, the Spatially Variant Mixture Model (SVMM) is applied in the bootstrap samples and the wavelet coefficients. The corresponding parameters are estimated by EM (Expectation Maximum) algorithm. Finally, a two-element Bayesian classifier is constructed. One part of the classifier is designed to exploit information in the original image, and the other part is designed to exploit information obtained in the wavelet domain. Theoretic analysis and experimental results confirms that the approach is efficient for infrared image segmentation, robust to noise and less computationally involved.