Efficient greedy learning of Gaussian mixture models
Neural Computation
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A Remote Sensing image change detection method based on contexture information is proposed. The difference image is constructed by PCA and subtraction operation. Firstly, the Hidden Markov Random Field (HMRF) model is applied to characterize the contexture-dependent information, and the Energy function of system is defined. Secondly, the Greedy EM algorithm is used to overcome the disadvantage of the EM algorithm that assumed the number of the mixture components is a known priori, the performance of the overall parameter estimation process depends on the given good initial settings excessively, and the estimated parameter can be resulted from some local optimum points. The distribution model structure and parameters are learned accurately to finds the best fit of the given data. Finally the changed area is obtained by using Iterated Conditional Modes (ICM) to optimize the energy function. Experiments show that the proposed method has virtues of preserving structural change and filtering noises.