Abstract
Concerning the high time complexity of inference and parameter estimation in graph model,the concept of superpixel was introduced into the Conditional Random Field(CRF),and a superpixel-based CRF image classification method was proposed.This method first over segmented the image into small homogeneous regions which were called superpixels by using mean shift method.Then the graphical model was constructed with superpixels as nodes and the neighboring nodes as edges.The corresponding definition of CRF and the methods for parameter estimation and labeling inference were proposed and implemented.The experimental results show that better classification results are obtained by the superpixel-based CRF model.At the same time,running time is largely reduced.
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