基于随机纹理的代价滤波式抠图

陈秋凤; 申群太; 刘鹏飞

doi:10.11999/JEIT150143

基于随机纹理的代价滤波式抠图

doi: 10.11999/JEIT150143

1.
(中南大学信息科学与工程学院长沙 410083) ②(中国人民解放军95856部队南京 210028)

基金项目:

国家自然科学基金(61473318, 60974048)

计量
- 文章访问数: 1498
- HTML全文浏览量: 128
- PDF下载量: 393
- 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

Cost Filtered Matting with Radom Texture Features

1.
(School of Information Science and Engineering, Central South University, Changsha 410083, China)

Funds:

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

摘要

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。
- 图像处理 /
- 纹理抠图 /
- 随机投影 /
- 代价滤波 /
- 迭代平滑
Abstract: In order to deal with the color overlap problem in matting, a fast random projection method is proposed to complement the color information. First, the raw texture matrix is obtained through dense abstraction from color image. The random projection is performed and the best three texture channels are chosen by the foreground and background overlap factors. Combining the texture image, the new cost function takes into account texture, color, and spatial information. Second, the filtering process is carried out to the sample selection cost, including the effect of the local and nonlocal neighbors. Finally, the relationship between iterative filter and global energy smooth is proven, and the post filter formula is obtained. Experiments show that the cost filtered matting with random texture features produces both visually and quantitatively better results when the color distributions of the foreground and background are similar.
- Image processing /
- Texture matting /
- Random projection /
- Cost filtering /
- Iterative smooth

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参考文献(16)

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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