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基于随机纹理的代价滤波式抠图

陈秋凤 申群太 刘鹏飞

陈秋凤, 申群太, 刘鹏飞. 基于随机纹理的代价滤波式抠图[J]. 电子与信息学报, 2015, 37(11): 2578-2586. doi: 10.11999/JEIT150143
引用本文: 陈秋凤, 申群太, 刘鹏飞. 基于随机纹理的代价滤波式抠图[J]. 电子与信息学报, 2015, 37(11): 2578-2586. doi: 10.11999/JEIT150143
Chen Qiu-feng, Shen Qun-tai, Liu Peng-fei. Cost Filtered Matting with Radom Texture Features[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2578-2586. doi: 10.11999/JEIT150143
Citation: Chen Qiu-feng, Shen Qun-tai, Liu Peng-fei. Cost Filtered Matting with Radom Texture Features[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2578-2586. doi: 10.11999/JEIT150143

基于随机纹理的代价滤波式抠图

doi: 10.11999/JEIT150143
基金项目: 

国家自然科学基金(61473318, 60974048)

Cost Filtered Matting with Radom Texture Features

Funds: 

The National Natural Science Foundation of China (61473318, 60974048)

  • 摘要: 该文针对抠图中前背景颜色歧义这一难题,提出快速随机纹理算法来对颜色信息进行有效的补偿,先对原始图像进行稠密抽取得到初始纹理,后经随机投影降维,再根据前背景交叠度选择最优通道生成随机纹理图。结合生成的纹理信息,设计了空间、颜色、纹理联合样本选择指标。接着,综合考虑局部近邻和非局部近邻的作用,对样本选择代价进行滤波。最后论证近邻迭代滤波与全局能量方程平滑的关系,推导了后期迭代平滑公式。实验结果表明,基于随机纹理的代价滤波式抠图在前背景颜色分布近似时,能够取得视觉和定量上更好的结果。
  • Levin A, Lischinski D, and Weiss Y. A closed form solution to natural image matting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 30(2): 61-68.
    Shi Y, Au O C, Pang J, et al.. Color clustering matting[C]. IEEE International Conference on Multimedia and Expo, California, USA, 2013, 7: 1-6.
    Wang J and Cohen M F. Optimized color sampling for robust matting[C]. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007, 6: 281-288.
    He B,Wang G J, and Zhang C. Iterative transductive learning for automatic image segmentation and?matting?with RGB-D data[J]. Journal of Visual Communication and Image Representation, 2014, 25(5): 1031-1043.
    Shahrian E, Rajan D, Price B, et al.. Improving image matting using comprehensive sampling sets[C]. Conference on Computer Vision and Pattern Recognition, Oregon, Portland, USA, 2013, 6: 636-643.
    Shahrian E and Rajan D. Weighted color and texture sample selection for image matting[C]. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, USA, 2012, 6: 718-725.
    Rhemann C, Rother C, and Gelautz M. Improving color modeling for alpha matting[C]. The British Machine Vision Conference, Leeds, UK, 2008, 9: 1155-1164.
    He K, Rhemann C, Rother C, et al.. A global sampling method for alpha matting[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado, USA, 2011, 6: 2049-2056.
    Gastal E S and Oliveira M M. Shared sampling for real‐time alpha matting[J]. Eurographics, 2010, 29(2): 575-584.
    Jubin J, Deepu R, and Hisham C. Sparse codes as alpha mattes[C]. The British Machine Vision Conference, Nottingham, England, 2014, 9: 1-11.
    Varma M and Zisserman A. A statistical approach to material classification using image patches[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2032-2047.
    Liu L and Paul W. Texture classification from random features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 574-586.
    Hosini A, Bleyer M, Rother C, et al.. Fast cost-volume filtering for visual correspondence and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(2): 504-511.
    Dasgupta S and Gupta A. An elementary proof of a theorem of Johnson and Lindenstrauss[J]. Random Structures and Algorithms, 2003, 22(1): 60-65.
    Sural S, Qian G, and Pramanik S. Segmentation and histogram generation using the HSV color space for image retrieval[C]. International Conference on Image Processing, New York, USA, 2002, 2: 589-592.
    Marius M and David GL. Fast approximate nearest neighbors with automatic algorithm configuration[C]. International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, 2009, 2: 331-340.
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  • 被引次数: 0
出版历程
  • 收稿日期:  2015-01-27
  • 修回日期:  2015-06-29
  • 刊出日期:  2015-11-19

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