结合CRF与ShapeBM形状先验的图像标记

doi:10.11959/j.issn.1000-0801.2017004

Abstract

Abstract:

Conditional random field (CRF) is a powerful model for image labeling,it is particularly well-suited to model local interactions among adjacent regions (e.g.superpixels).However,CRF doesn't consider the global constraint of objects.The overall shape of the object is used as a global constraint,the ShapeBM can be taken advantage of modeling the global shape of object,and then a new labeling model that combined the above two types of models was presented.The combination of CRF and ShapeBM was based on the superpixels,through the pooling technology was wed to establish the corresponding relationship between the CRF superpixel layer and the ShapeBM input layer.It enhanced the effectiveness of the combination of CRF and ShapeBM and improved the accuracy of the labeling.The experiments on the Penn-Fudan Pedestrians dataset and Caltech-UCSD Birds 200 dataset demonstrate that the model is more effective and efficient than others.

Key words: CRF, ShapeBM, join model, superpixels, image labeling

CLC Number:

TP391

Hao WANG,Lijun GUO,Yadong WANG,Rong ZHANG. CRF combined with ShapeBM shape priors for image labeling[J]. Telecommunications Science, 2017, 33(1): 67-76.

Figures/Tables 8

References 26

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方法	超像素标记准确率	误识率
CRF	83.97%	参照
SCRF	86.31%	14.60%
ShapeBM	85.01%	6.49%
本文方法	87.90%	24.52%

方法	超像素准确率	误识率
CRF	78.85%	参照
SCRF	81.93%	14.60%
ShapeBM	81.27%	11.44%
本文方法	83.34%	21.23%

CRF combined with ShapeBM shape priors for image labeling

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