The HMM parameters are estimated by the EM algorithm. Thus, the dependency in two dimensions is reflected simultaneously. The HMM considers feature vectors statistically dependent through an underlying state process assumed to be a Markov mesh, which has transition probabilities conditioned on the states of neighboring blocks from both horizontal and vertical directions. In order to improve classification by context, an algorithm is proposed that models images by two dimensional (2-D) hidden Markov models (HMM’s). Conventional block-based classification algorithms decide the class of a block by examining only the feature vector of this block and ignoring context information. Abstract-For block-based classification, an image is divided into blocks, and a feature vector is formed for each block by grouping statistics extracted from the block.
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