多层次MRF重标记及映射法则下的图像分割

姚婷婷; 谢昭

doi:10.3724/SP.J.1004.2013.01581

多层次MRF重标记及映射法则下的图像分割

doi: 10.3724/SP.J.1004.2013.01581

姚婷婷,
谢昭

1.
合肥工业大学计算机与信息学院合肥 230009

基金项目:

国家自然科学基金(60905005, 61273237),教育部博士点基金(20090 111110015), 中央高校基本科研业务费专项资金(2012HGCX0001) 资助

详细信息

作者简介:
姚婷婷合肥工业大学博士研究生.主要研究方向为图像理解,计算机视觉,模式识别.E-mail:ytt999@yahoo.com.cn

计量
- 文章访问数: 2734
- HTML全文浏览量: 74
- PDF下载量: 4391
- 被引次数: 0
出版历程
- 收稿日期: 2012-05-21
- 修回日期: 2012-08-31
- 刊出日期: 2013-10-20

Top-down Inference with Relabeling and Mapping Rules in Hierarchical MRF for Image Segmentation

1.
Department of Computer and Information, Hefei University of Technology, Hefei 230009

Funds:

Supported by National Natural Science Foundation of China (60905005, 61273237), Research Fund for the Doctoral Program of Higher Education of China (20090111110015), and Fundamen-tal Research Funds for the Central Universities (2012HGCX00 01)

摘要

摘要: 针对彩色图像分割问题,研究Markov 随机场(Markov random fields, MRF)模型内迭代条件模式(Iterative conditional mode, ICM)方法的标记推理策略. 通过小波分解构造图像多尺度表达,针对顶层图像先验标记获取问题,改进原始谱聚类算法, 通过近邻传播自动确定图像的聚类参数,运用集成学习提高算法的稳定性和准确度. 对其他各尺度图像,通过分析尺度关联下的区域特征变化,结合不同尺度间的特征相似性和同一尺度内空间邻域的一致性, 提出一种立体结构描述下的尺度--空间映射法则.通过定量和定性的分割实验,结果表明本文算法具有良好的准确性、鲁棒性和普适性.
- 层次Markov随机场 /
- 集成标记 /
- 层间映射推理 /
- 图像分割
Abstract: For the purpose of color images segmentation, the problem of inferring strategy for iterative conditional mode (ICM) algorithm in the Markov random fields (MRFs) is revisited. Using wavelet decomposition to construct multi-solution expressing of image, the original spectral clustering algorithm is improved to solve the problem of acquiring the prior labels for top-scale image. The parameters of image clustering are generated by affine propagation automatically and the prior labels are acquired after optimizing with ensemble learning to improve the accuracy and stability of algorithm. By analyzing the difference of features in related scales, a scale-space mapping algorithm is proposed to combine the similarity between the connected scales with the consistency in the same scale. With the quantization and evaluation of the segmentation results, the algorithm shows its property of stability, accuracy, generality and robustness to the noise disturbance.
- Hierarchical Markov random field (MRF) /
- ensemble labeling /
- label-mapping inference /
- image segmentation

HTML全文

参考文献(24)

[1]	Gao Jun, Xie Zhao. Image Understanding Theory and Method. Beijing: Science Press, 2009. 233-287(高隽, 谢昭. 图像理解理论与方法. 北京: 科学出版社, 2009. 233-287)
[2]	Gong M L, Li C. Foreground segmentation of live videos using locally competing 1SVMs. In: Proceedings of the 2011 Computer Vision and Pattern Recognition. Colorado, USA: IEEE, 2011. 2105-2112
[3]	Wang Kun-Feng, Li Zhen-Jiang, Tang Shu-Ming. Visual traffic data collection approach based on multi-features fusion. Acta Automatica Sinica, 2011, 37(3): 322-330(王坤峰, 李镇江, 汤淑明. 基于多特征融合的视频交通数据采集方法. 自动化学报, 2011, 37(3): 322-330)
[4]	Bu Jiang, Lao Song-Yang, Bai Liang, Liu Gang. Automatic camera calibration for broadcast soccer video using playfield model. Acta Automatica Sinica, 2012, 33(3): 321-330(卜江, 老松杨, 白亮, 刘钢. 一种基于球场模型的广播足球视频摄像机自动定标方法. 自动化学报, 2012, 38(3): 321-330)
[5]	Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619
[6]	Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation. International Journal of Computer Vision, 2004, 59(2): 167-181
[7]	Shi J B, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905
[8]	Stuart G, Donald G. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6): 721-741
[9]	Liu Song-Tao, Yin Fu-Liang. The basic principle and its new advances of image segmentation methods based on graph cuts. Acta Automatica Sinica, 2012, 38(6): 911-922(刘松涛, 殷福亮. 基于图割的图像分割方法及其新进展. 自动化学报, 2012, 38(6): 911-922)
[10]	Li Q S, Liu G Y. Multi-resolution Markov random field model with variable potentials in wavelet domain for texture image segmentation. In: Proceedings of the 2010 International Conference on Computer Application and System Modeling. Taiyuan, China: IEEE, 2010, 9: 342-346
[11]	Wang L, Liu J. Texture segmentation based on MRMRF modeling. Pattern Recognition Letters, 2000, 21(2): 189-200
[12]	Gigandet X, Cuadra M B, Pointet A, Cammoun L, Caloz R, Thiran J. Region-based satellite image classification: method and validation. In: Proceedings of the 2005 IEEE International Conference on Image Processing. Antalya, Turkey: IEEE, 2005, 3: 832-835
[13]	Cressie N, Verzelen N. Conditional-mean least-squares fitting of Gaussian Markov random fields to Gaussian fields. Computational Statistics and Data Analysis, 2008, 52(5): 2794-2807
[14]	Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, 1986, 48(3): 259-302
[15]	Von Luxburg U. A tutorial on spectral clustering. Statistics and Computing 2007, 17(4): 395-416
[16]	Frey B J, Dueck D. Clustering by passing messages between data points. Science, 2007, 315(5814): 972-976
[17]	Zhou Z H, Tang W. Clusterer ensemble. Knowledge-Based Systems, 2006, 19(1): 77-83
[18]	Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239
[19]	Kolmogorov V, Zabih R. What energy functions can be minimized via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 147-159
[20]	Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1124-1137
[21]	Tu Z W, Zhu S C. Image segmentation by data-driven markov chain Monte Carlo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 657-673
[22]	Jung M, Yun E J, Kim C S. Multiresolution approach for texture segmentation using MRF models. In: Proceedings of the 2005 IEEE Geoscience and Remote Sensing Symposium. Seoul, Korea: IEEE, 2005. 3971-3974
[23]	Martin D R, Fowlkes C, Tal D, Malik J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision. Vancouver, Canada: IEEE, 2001. 416-423
[24]	Cho M, MuLee M K. Authority-shift clustering: hierarchical clustering by authority seeking on graphs. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 3193-3200