基于隨機(jī)紋理的代價(jià)濾波式摳圖
doi: 10.11999/JEIT150143 cstr: 32379.14.JEIT150143
-
1.
(中南大學(xué)信息科學(xué)與工程學(xué)院 長(zhǎng)沙 410083) ②(中國(guó)人民解放軍95856部隊(duì) 南京 210028)
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61473318, 60974048)
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: 該文針對(duì)摳圖中前背景顏色歧義這一難題，提出快速隨機(jī)紋理算法來(lái)對(duì)顏色信息進(jìn)行有效的補(bǔ)償，先對(duì)原始圖像進(jìn)行稠密抽取得到初始紋理，后經(jīng)隨機(jī)投影降維，再根據(jù)前背景交疊度選擇最優(yōu)通道生成隨機(jī)紋理圖。結(jié)合生成的紋理信息，設(shè)計(jì)了空間、顏色、紋理聯(lián)合樣本選擇指標(biāo)。接著，綜合考慮局部近鄰和非局部近鄰的作用，對(duì)樣本選擇代價(jià)進(jìn)行濾波。最后論證近鄰迭代濾波與全局能量方程平滑的關(guān)系，推導(dǎo)了后期迭代平滑公式。實(shí)驗(yàn)結(jié)果表明，基于隨機(jī)紋理的代價(jià)濾波式摳圖在前背景顏色分布近似時(shí)，能夠取得視覺(jué)和定量上更好的結(jié)果。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.
-
Key words:
- Image processing /
- Texture matting /
- Random projection /
- Cost filtering /
- Iterative smooth
-
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. -
計(jì)量
- 文章訪問(wèn)數(shù): 1559
- HTML全文瀏覽量: 141
- PDF下載量: 396
- 被引次數(shù): 0
下載:
