CRF combined with ShapeBM shape priors for image labeling

Hao WANG; Lijun GUO; Yadong WANG; Rong ZHANG

doi:10.11959/j.issn.1000-0801.2017004

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CRF combined with ShapeBM shape priors for image labeling

research and development | 更新时间：2024-06-05

- CRF combined with ShapeBM shape priors for image labeling
- Telecommunications Science Vol. 33, Issue 1, Pages: 67-76(2017)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2017004
  CLC： TP391
- Published Online：2017-01，
  
  Published：15 January 2017
- 稿件说明：
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Hao WANG, Lijun GUO, Yadong WANG, et al. CRF combined with ShapeBM shape priors for image labeling[J]. Telecommunications science, 2017, 33(1): 67-76.
DOI：

Hao WANG, Lijun GUO, Yadong WANG, et al. CRF combined with ShapeBM shape priors for image labeling[J]. Telecommunications science, 2017, 33(1): 67-76. DOI： 10.11959/j.issn.1000-0801.2017004.

摘要

条件随机场（CRF）是一种强大的图像标记模型，适合描述图像相邻区域间（例如超像素）的相互作用。然而，CRF没有考虑标记对象的全局约束。对象的整体形状可以作为对象标记的一种全局约束，利用形状玻尔兹曼机（ShapeBM）在建模对象的整体形状方面的优势，提出了一种CRF与ShapeBM相结合的标记模型。标记模型建立在超像素的基础上，并通过pooling技术在CRF的超像素层与ShapeBM的输入层间建立对应关系，增强了 CRF 与 ShapeBM 结合的有效性，提高了标记准确率。在 Penn-Fudan Pedestrians 数据集和 Caltech-UCSD Birds 200数据集上的实验结果表明，联合模型明显地改善了标记结果。

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.

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references

LAFFERTY J , MCCALLUM A , PEREIRAF C , et al . Conditional random fields:probabilistic models for segmenting and labeling sequence data [C]// ICML,June 28-July 1,2001,Williams College,UK . New Jersey : IEEE Press , 2001 .

BORENSTEIN E , SHARON E , ULLMAN S , et al . Combining top-down and bottom-up segmentation [C]//Conference on Computer Vision and Pattern Recognition,June 27-July 2,2004,Washington,DC,USA . New Jersey : IEEE Press , 2004 .

HE X , ZEMEL R S , CARREIRAPERPINAN M A , et al . Multi-scale conditional random fields for image labeling[C]//Conference on Computer Vision and Pattern Recognition,June 27-July 2,2004,Washington,DC,USA . New Jersey : IEEE Press , 2004 .

HE X , ZEMEL R S , RAY D , et al . Learning and incorporating top-down cues in image segmentation[C]// European Conference on Computer Vision,May 7-13,2006,Graz,Austria . New Jersey : IEEE Press , 2006 : 338 - 351 .

SHOTTON J , WINN J , ROTHER C , et al . Texton boost for image understanding:multi-class object recognition and segmentation by jointly modeling texture,layout,and context [J ] . International Journal of Computer Vision , 2009 , 81 ( 1 ): 2 - 23 .

ZHANG L . A unified probabilistic graphical model and its application to image segmentation [J ] . Rensselaer Polytechnic Institute , 2009 ( 3 ).

SMOLENSKY P . Information processing in dynamical systems:foundations of harmony theory [M ] . Cambride : MIT Press , 1986 ( 1 ): 194 - 281 .

SALAKHUTDINOV R , HINTON G E . Deep Boltzmann machines [J ] . Journal of Machine Learning Research , 2009 , 5 ( 2 ): 1967 - 2006 .

ESLAMI S M , HEESS N , WILLIAMS C K , et al . The shape Boltzmann machine:a strong model of object shape [J ] . International Journal of Computer Vision , 2014 , 107 ( 2 ): 155 - 176 .

KAE A , SOHN K , LEE H , et al . Augmenting CRFs with Boltzmann machine shape priors for image labeling [C]//Conference on Computer Vision and Pattern Recognition,June 23-28,2013,Portland,Oregon,USA . New Jersey : IEEE Press , 2013 .

KAE A , MARLIN B M , LEARNEDMILLER E G , et al . The shape-time random field for semantic video labeling [C]//Conference on Computer Vision and Pattern Recognition,June 23-28,2014,Columbus,OH,USA . New Jersey : IEEE Press , 2014 .

CHEN F , YU H , HU R , et al . Deep learning shape priors for object segmentation [C]//Conference on Computer Vision and Pattern Recognition,June 23-28,2013,Portland,Oregon,USA . New Jersey : IEEE Press , 2013 .

CREMERS D , SCHMIDT F R , BARTHEL F , et al . Shape priors in variational image segmentation:Convexity,Lipschitz continuity and globally optimal solutions [C]//Conference on Computer Vision and Pattern Recognition,June 24-26,2008,Anchorage,Alaska,USA . New Jersey : IEEE Press , 2008 .

ARBELAEZ P , HARIHARAN B , GU C , et al . Semantic segmentation using regions and parts [C]//Conference on Computer Vision and Pattern Recognition,June 16-21,2012,Providence,RI,USA . New Jersey : IEEE Press , 2012 .

MURPHY K , WEISS Y , JORDAN M I , et al . Loopy beliefpropagation for approximate inference:an empirical study[C]//15th Conference on Uncertainty in Artificial Intelligence,July 30-August 1,1999,Stockholm,Sweden . New Jersey : IEEE Press , 1999 .

SCHMIDT M . minFunc:unconstrained differentiable multivariate optimization in Matlab [EB/OL ] . 2016 - 07 - 10 . http://www.di.ens.fr/~mschmidt/Software/minFunc.html. http://www.di.ens.fr/~mschmidt/Software/minFunc.html.

SAUL L K , JAAKKOLA T S , JORDAN M I , et al . Mean field theory for sigmoid belief networks [J ] . Journal of Artificial Intelligence Research , 1996 ( 13 ).

MNIH V , LAROCHELLE H , HINTON G E , et al . Conditional restricted Boltzmann machines for structured output prediction[C]//Conference on Uncertainty in Artificial Intelligence,Aug 15-17,2012,Catalina Island,USA . New Jersey : IEEE Press , 2012 .

WANG L , SHI J , SONG G , et al . Object detection combining recognition and segmentation[C]//Asian Conference on Computer Vision,November 18-22,2007,Tokyo,Japan . New Jersey : IEEE Press , 2007 .

WELINDER P , BRANSON S , MITA T , et al . Caltech-UCSD Birds 200 [J ] . California Institute of Technology , 2010 .

YANG J , SAFAR S , YANG M H . Max-margin Boltzmann machines for object segmentation [C]//IEEE Conference on Computer Vision and Pattern Recognition,June 23-28,2014,Columbus,OH,USA . New Jersey : IEEE Press , 2014 .

ACHANTA R P , SHAJI A , SMITH K M , et al . SLIC superpixels compared to state-of-the-art superpixelmethods [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2012 , 34 ( 11 ): 2274 - 2282 .

MALIK J , BELONGIE S , SHI J , et al . Textons,contours and regions:cue integration in image segmentation [C]//ICCV,September 20-25,1999,Kerkyra,Corfu,Greece . New Jersey : IEEE Press , 1999 .

MARTIN D R , FOWLKES C C , MALIK J , et al . Learning to detect natural image boundaries using brightness and texture[C]//Conference on Neural Information Processing Systems,December 8-13,2003,Providence,USA . New Jersey : IEEE Press , 2003 .

HUANG G B , NARAYANA M , LEARNEDMILLER E G , et al . Towards unconstrained face recognition [C]//Conference on Computer Vision and Pattern Recognition,June 24-26,2008,Anchorage,Alaska,USA . New Jersey : IEEE Press , 2008 .

COHN T . Efficient inference in large conditional random Fields [M ] . Berlin : Springer , 2006 : 606 - 613 .

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