基于超像素的条件随机场图像分类

Superpixel-based conditional random field for image classification

针对图模型在推导和参数估计中时间复杂度较高的问题,在条件随机场(CRF)中引入了超像素的概念,提出了一种基于超像素的CRF图像分类方法。该方法首先通过均值漂移算法将图像过分割成小的均匀区域(称为超像素),然后以超像素为节点、空间相邻的节点以边连接建立图模型,给出了相应的CRF的定义,实现了模型的参数估计和推导。实验结果表明,基于超像素的CRF模型在得到较好分类结果的同时,极大地缩短了运行时间,提高了效率。

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 estimatioh 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.