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Conditional Random Fields for Image Labeling

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  • Tong Liu
  • Xiutian Huang
  • Jianshe Ma

Abstract

With the rapid development and application of CRFs (Conditional Random Fields) in computer vision, many researchers have made some outstanding progress in this domain because CRFs solve the classical version of the label bias problem with respect to MEMMs (maximum entropy Markov models) and HMMs (hidden Markov models). This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean field approximation. This paper describes graph cut briefly while it introduces mean field approximation more detailedly which has a substantial speed of inference and is researched popularly in recent years.

Suggested Citation

  • Tong Liu & Xiutian Huang & Jianshe Ma, 2016. "Conditional Random Fields for Image Labeling," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-15, April.
    • Handle: RePEc:hin:jnlmpe:3846125
    DOI: 10.1155/2016/3846125
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    Cited by:

    1. Zhenzhen Cheng & Lijun Qi & Yifan Cheng, 2021. "Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds," Agriculture, MDPI, vol. 11(5), pages 1-19, May.

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