Conditional Random Fields for Pattern Recognition Applied to Structured Data
AbstractPattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples. View Full-Text
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Burr, T.; Skurikhin, A. Conditional Random Fields for Pattern Recognition Applied to Structured Data. Algorithms 2015, 8, 466-483.
Burr T, Skurikhin A. Conditional Random Fields for Pattern Recognition Applied to Structured Data. Algorithms. 2015; 8(3):466-483.Chicago/Turabian Style
Burr, Tom; Skurikhin, Alexei. 2015. "Conditional Random Fields for Pattern Recognition Applied to Structured Data." Algorithms 8, no. 3: 466-483.