An Image Co-Segmentation Algorithm Using Hyper-Graphcut

doi:10.7652/xjtuxb201402004

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An Image Co-Segmentation Algorithm Using Hyper-Graphcut

更新时间：2025-07-09

- An Image Co-Segmentation Algorithm Using Hyper-Graphcut
- Vol. 48, Issue 2, Pages: 20-24+37(2014)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201402004
  CLC： TP391.41
- Online First：10 February 2014，
  
  Published：2014
- 稿件说明：
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An Image Co-Segmentation Algorithm Using Hyper-Graphcut[J]. 2014, 48(2): 20-24+37.
DOI：

An Image Co-Segmentation Algorithm Using Hyper-Graphcut[J]. 2014, 48(2): 20-24+37. DOI： 10.7652/xjtuxb201402004.

摘要

针对基于传统图割的图像共分割算法存在计算复杂度高而导致分割效率低的问题

提出了一种基于超图的图像共分割算法

利用超图能更简洁有效地代表图像中特征关系的特性来提高图像共分割的效率。该算法首先对具有相似前景的2幅图像分别进行Mean-shift过分割

并将得到的过分割区域分块作为超图的节点; 然后利用分块的颜色直方图计算所有分块间的相似性

并将相似的分块对应的相似节点集合和单幅图像中相邻节点集合作为超边并计算其权值

构造超图; 最后利用基于谱分析的近似算法求解超图归一化分割问题

获得图像对的共分割结果。实验结果表明

所提算法相比于单幅图像的归一化分割算法以及基于传统图割的图像共分割算法具有更好的分割效果

同时分割时间缩短至少45%。

Abstract

An image co-segmentation algorithm using hyper-graphcut is proposed to improve the efficiency of image co-segmentation algorithms based on traditional graph-cut for the high computational complexity in optimizing energy functions. The algorithm bases on the property that hyper-graphs represent the relationship among images better. Mean-shift over-segmentation is applied to the input image pair to obtain image patches and these patches are then expressed as the nodes of a hyper-graph. Then the similarities between patches are calculated through their color histogram features

and the collections of similar patches and their neighbor patches are set as hyper-edges and a hyper-graph is constructed. The spectral analysis algorithm is performed on the constructed hyper-graph to obtain the final co-segmentation result of the image pair. Experimental results and comparisons with the normalized-cut of single image and the co-segmentation algorithm based on graph-cut show that the proposed algorithm has a notable improvement on image co-segmentation efficiency with a calculation cost reduction at least 45%.

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references

林开颜, 吴军辉, 徐立鸿等. 彩色图像分割方法综述[J]. 中国图象图形学报, 2005, 10(1): 1-10.

LIN Kaiyan, WU Junhui, XU Lihong. A survey on color image segmentation techniques[J]. Journal of Image and Graphics, 2005, 10(1): 1-10.

ROTHER C, MINKA T, BLAKE A, et al. Cosegmentation of image pairs by histogram matching-incorporating a global constraint into MRFs[C]∥Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2006: 993-1000.

HOCHBAUM D S, SINGH V. An efficient algorithm for co-segmentation[C]∥Proceedings of 2009 IEEE 12th International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 2009: 269-276.

CHANG K Y, LIU T L, LAI S H. From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model[C]∥Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2011: 2129-2136.

VICENTE S, ROTHER C, KOLMOGOROV V. Object cosegmentation[C]∥Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2011: 2217-2224.

JOULIN A, BACH F, PONCE J. Discriminative clustering for image co-segmentation[C]∥Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2010: 1943-1950.

HUANG Hua, ZHANG Lei, ZHANG Hongchao. RepSnapping: efficient image cutout for repeated scene elements[J]. Computer Graphics Forum, 2011, 30(7): 2059-2066.

张洪超, 张磊, 黄华. 基于跨时空域相似邻接图的视频分割方法[J]. 图学学报, 2012, 33(2): 83-88.

ZHANG Hongchao, ZHANG Lei, HUANG Hua. Video cutout method based on spatio-temporal similarity neighboring graph[J]. Journal of Graphics, 2012, 33(2): 83-88.

BERGE C. Graph and hypergraph[M]. Amsterdam, Holland: North-Holland Publishing Company, 1973.

COMANICIU D, MEER P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.

SHI J, MALIK J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.

汪粼波, 郭延文, 夏天辰, 等. 样本驱动的半自动图像集前背景分割[J]. 计算机辅助设计与图形学学报, 2013, 25(6): 794-801.

WANG Linbo, GUO Yanwen, XIA Tianchen, et al. Example-driven semi-automatic image collection segmentation[J]. Journal of Computer-Aided Design Computer Graphics, 2013, 25(6): 794-801.

郝红侠,刘芳,焦李成,等.采用结构自适应窗的非局部均值图像去噪算法.2013,47(12):71-76.[doi:10.7652/xjtuxb 201312013]

刘光辉,任庆昌,孟月波,等.自适应先验马尔可夫随机场模型的图像分割算法.2013,47(10):62-67.[doi:10.7652/xjtuxb201310011]

侯兴松,张兰,肖琳.合成孔径雷达图像的贝叶斯压缩感知重构算法.2013,47(8):74-79.[doi:10.7652/xjtuxb201308013]

穆为磊,高建民,王昭,等.考虑人眼视觉特性的射线检测数字图像质量评价方法.2013,47(7):91-95.[doi:10.7652/xjtuxb201307017]

赵军,赵继,张雷.焊缝结构光图像处理与特征提取方法.2013,47(1):114-119.[doi:10.7652/xjtuxb201301022]

王梦琪,曹良志,吴宏春,等.三维任意几何光子成像系统的散射修正及图像重建方法.2012,46(11):86-90.[doi:10.7652/xjtuxb201211017]

李玉花,齐春.利用位置字典对的人脸图像超分辨率方法.2012,46(6):7-11.[doi:10.7652/xjtuxb201206002]

史金钢,齐春.一种双约束稀疏模型图像修复算法.2012,46(2):6-10.[doi:10.7652/xjtuxb201202002]

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