Abstract
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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© 2006 International Federation for Information Processing
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Wen, M., Han, H., Wang, L., Wang, W. (2006). 2D Conditional Random Fields for Image Classification. In: Shi, Z., Shimohara, K., Feng, D. (eds) Intelligent Information Processing III. IIP 2006. IFIP International Federation for Information Processing, vol 228. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-44641-7_40
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DOI: https://doi.org/10.1007/978-0-387-44641-7_40
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-44639-4
Online ISBN: 978-0-387-44641-7
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