基于鲁棒随机游走的交互式植物叶片分割

doi:10.16451/j.cnki.issn1003-6059.201810007

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摘要

受背景、阴影和反射等不同成像环境影响,固定模型难以解决植物叶片分割问题.针对此情况,文中提出基于鲁棒随机游走的交互式植物叶片分割方法,采用交互式策略对用户指定像素先验信息进行传播,通过鲁棒随机游走算法实现植物叶片分割.首先,基于随机游走算法构建成对像素的关系,建立一个超像素一致性约束模型,促使分割图像边缘更光滑.然后,通过人机交互获取指定像素先验信息.最后,利用对数似然比预测像素属于背景的概率,并用于指导标签传播.在宽松受控和非受控环境植物叶片图像上的实验表明,文中方法可以更好地得到光滑、鲁棒的植物叶片分割图.

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	胡静
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	杨猛

关键词 ：随机游走, 超像素, 对数似然比, 叶片分割

Abstract：

Segmenting the plant leaf by a fixed model is problematic due to different imaging conditions, such as backgrounds, shadows and reflections. In this paper,interactive plant leaf segmentation method based on robust random walk is proposed. The interactive strategy is employed to propagate prior information of user-specified pixel, and robust random walk algorithm is used to realize plant leaf segmentation. The relationship of pairwise pixels is considered based on random walk and a super-pixel-consistent constraint is added to make the edges of segmentation smooth. Since the random walk algorithm only considers pairwise pixel relations, it is sensitive to the specified and connected pixels. The prior information of specified pixel is obtained through human-machine interaction. On the basis of random walk model, a log-likelihood ratio is used to predict the probability of a pixel belonging to the background and guide the label propagation. Experiments on plant leaf images in loose controlled and uncontrolled environments show that the proposed method obtains smoother and more robust plant leaf segmentation images than other methods.

收稿日期: 2018-04-09

ZTFLH:

TP 391.41

作者简介: 胡静,博士研究生,副教授,主要研究方向为图像处理、模式识别.E-mail:279641292@qq.com.;陈志泊(通讯作者),博士,教授,主要研究方向为数据库技术、计算机软件与理论、图像处理、模式识别.E-mail:zhibo@bjfu.edu.cn.;张荣国,博士,教授,主要研究方向为图像处理、模式识别.E-mail:rg-zh@163.com.;杨猛,博士,副教授,主要研究方向为计算机图形学、虚拟现实等.E-mail:yangmeng@bjfu.edu.cn.

引用本文:

胡静,陈志泊,张荣国,杨猛. 基于鲁棒随机游走的交互式植物叶片分割[J]. 模式识别与人工智能, 2018, 31(10): 933-940. HU Jing, CHEN Zhibo, ZHANG Rongguo, YANG Meng. Interactive Leaf Segmentation Using Robust Random Walk. , 2018, 31(10): 933-940.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201810007 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I10/933

[1] BUONCOMPAGNI S, MAIO D, LEPETIT V. Leaf Segmentation under Loosely Controlled Conditions[C/OL]. [2018-03-25]. http://www.bmva.org/bmvc/2015/papers/paper133/abstract133.pdf.
[2] KUMAR N, BELHUMEUR P N, BISWAS A, et al. Leafsnap: A Computer Vision System for Automatic Plant Species Identification // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2012: 502-516.
[3] GRAND-BROCHIER M, VACAVANT A, CERUTTI G, et al. Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods. IEEE Transactions on Image Processing, 2015, 24(5): 1549-1560.
[4] 王颖.基于局部学习的图像编辑算法研究.博士学位论文.北京:中国科学院大学, 2012.
(WANG Y. Researches on Image Editing with Local Learning. Ph.D. Dissertation. Beijing, China: University of Chinese Academy of Sciences, 2012.)
[5] 贾丽丽,余孝源,梁耀,等.自然生长状态下树叶图像的分割与提取.华南师范大学学报(自然科学版), 2017, 49(1): 116-121.
(JIA L L, YU X Y, LIANG Y, et al. Leaves′ Image Segmentation and Extraction under Natural Growing Condition. Journal of South China Normal University(Natural Science Edition), 2017, 49(1): 116-121.)
[6] 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.
[7] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 2004, 59(2): 167-181.
[8] ARBELAEZ P, MAIRE M, FOWLKES C C, et al. Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898-916.
[9] BOYKOV Y, FUNKA-LEA G. Graph Cuts and Efficient ND Image Segmentation. International Journal of Computer Vision, 2006, 70(2): 109-131.
[10] GRADY L. Random Walks for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(11): 1768-1783.
[11] ROWEIS S T, SAUL L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326.
[12] SCHARR H, MINERVINI M, FRENCH A P, et al. Leaf Segmentation in Plant Phenotyping: A Collation Study. Machine Vision and Applications, 2016, 27(4): 585-606.
[13] GRADY L. Multilabel Random Walker Image Segmentation Using Prior Models // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005: 763-770.
[14] COLLINS M D, XU J, GRADY L, et al. Random Walks Based Multi-image Segmentation: Quasiconvexity Results and GPU-Based Solutions // Proc of the IEEE International Conference on Compu-ter Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 1656-1663.
[15] WU J J, ZHAO Y B, ZHU J Y, et al. MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 256-263.
[16] LEE C, JANG W D, SIM J Y, et al. Multiple Random Walkers and Their Application to Image Cosegmentation // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 3837-3845.
[17] GRADY L, SINOP A K. Fast Approximate Random Walker Segmentation Using Eigenvector Precomputation // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2008. DOI: 10.1109/CVPR.2008.4587487.
[18] SINOP A K, GRADY L. A Seeded Image Segmentation Framework Unifying Graph Cuts and Random Walker Which Yields A New Algorithm // Proc of the 11th IEEE International Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2007. DOI: 10.1109/ICCV.2007.4408927.
[19] YANG W X, CAI J F, ZHENG J M, et al. User-Friendly Interactive Image Segmentation through Unified Combinatorial User Inputs. IEEE Transactions on Image Processing, 2010, 19(9): 2470-2479.
[20] XIANG S M, NIE F P, ZHANG C X, et al. Interactive Natural Image Segmentation via Spline Regression. IEEE Transactions on Image Processing, 2009, 18(7): 1623-1632.
[21] XIANG S M, PAN C H, NIE F P, et al. Interactive Image Segmentation with Multiple Linear Reconstructions in Windows. IEEE Transactions on Multimedia, 2011, 13(2): 342-352.
[22] XIANG S M, NIE F P, ZHANG C S. Semi-supervised Classification via Local Spline Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(11): 2039-2053.
[23] BOYKOV Y Y, JOLLY M P. Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images // Proc of the 8th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2001: 105-112.
[24] BAI X, SAPIRO G. A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting // Proc of the 11th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2007. DOI: 10.1109/ICCV.2007.4408931.
[25] ACHANTA R, SHAJI A, SMITH K, et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.
[26] GORELICK L, VEKSLER O, BOYKOV Y, et al. Convexity Shape Prior for Binary Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2): 258-271.