CRF combined with ShapeBM shape priors for image labeling
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,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2017-01-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017004/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841530041342623744 |
|---|---|
| author | Hao WANG Lijun GUO Yadong WANG Rong ZHANG |
| author_facet | Hao WANG Lijun GUO Yadong WANG Rong ZHANG |
| author_sort | Hao WANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4260eaa5a4c841e0a7d9cfb3f21c8938 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2017-01-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-4260eaa5a4c841e0a7d9cfb3f21c89382025-01-15T03:13:30ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-01-0133677659603932CRF combined with ShapeBM shape priors for image labelingHao WANGLijun GUOYadong WANGRong ZHANGConditional 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017004/CRFShapeBMjoin modelsuperpixelsimage labeling |
| spellingShingle | Hao WANG Lijun GUO Yadong WANG Rong ZHANG CRF combined with ShapeBM shape priors for image labeling Dianxin kexue CRF ShapeBM join model superpixels image labeling |
| title | CRF combined with ShapeBM shape priors for image labeling |
| title_full | CRF combined with ShapeBM shape priors for image labeling |
| title_fullStr | CRF combined with ShapeBM shape priors for image labeling |
| title_full_unstemmed | CRF combined with ShapeBM shape priors for image labeling |
| title_short | CRF combined with ShapeBM shape priors for image labeling |
| title_sort | crf combined with shapebm shape priors for image labeling |
| topic | CRF ShapeBM join model superpixels image labeling |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017004/ |
| work_keys_str_mv | AT haowang crfcombinedwithshapebmshapepriorsforimagelabeling AT lijunguo crfcombinedwithshapebmshapepriorsforimagelabeling AT yadongwang crfcombinedwithshapebmshapepriorsforimagelabeling AT rongzhang crfcombinedwithshapebmshapepriorsforimagelabeling |