2D Conditional Random Fields for Image Classification

For grid-based image classification, an image is divided into blocks, and a feature vector is formed for each block. Conventional grid-based classification algorithms suffer from inability to take into account the two-dimensional neighborhood interactions of blocks. We present a classification method based on two-dimensional Conditional Random Fields which can avoid the limitation. As a discriminative approach, the proposed method offers several advantages over generative approaches, including the ability to relax the assumption of conditional independence of the observations.

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