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.
CITATION STYLE
Wen, M., Han, H., Wang, L., & Wang, W. (2006). 2D conditional random fields for image classification. IFIP International Federation for Information Processing, 228, 383–390. https://doi.org/10.1007/978-0-387-44641-7_40
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