Image segmentation based on finite mixture models of nonparametric Hermite orthogonal sequence

Abstract

To solve the problem of over-reliance on priori assumptions of the parameter methods for finite mixture models, a nonparametric Hermite orthogonal sequence of mixture model for image segmentation method is proposed in this paper. First, the Hermite orthogonal sequence base on the image nonparametric mixture model is designed, and the mean integrated squared error(MISE) is used to estimate the smoothing parameter for each model; Second, the Expectation Maximum(EM) algorithm is used to estimate the orthogonal polynomial coefficients and the model of the weight. This method does not require any prior assumptions on the model, and it can effectively overcome the “model mismatch” problem. The experimental results with the images show that this method can achieve better segmentation results than the Gaussian Mixture Models method.