Video Matting via Sparse and Low-Rank Representation

Zou, Dongqing; Chen, Xiaowu; Cao, Guangying; Wang, Xiaogang

doi:10.1109/iccv.2015.183

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…However, these methods need to process the whole video together, which limits their effectiveness for applications that entail online processing, such as surveillance and action recognition. Another kind of approach for video target segmentation is video matting [3,37,41], which needs some human-computer interactions to obtain good segmentation performance. Nevertheless, in our work, we aim to automatically achieve online segmenting the moving target.…”

Section: Related Workmentioning

confidence: 99%

Multiple cues-based active contours for target contour tracking under sophisticated background

Zhao

Chen

et al. 2016

Vis Comput

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In this paper, we propose a novel target contour tracking method under sophisticated background using the multiple cues-based active contour model. To locate the target position, a contour-based mean-shift tracker is designed which combines both color and texture information. To reduce the adverse impact of sophisticated background and also accelerate the curve motion, we propose a twolayer-based target appearance model that combines both discriminative pre-learned-based global layer and votingbased local layer. The proposed appearance model is able to extract rough target region from the complex background, which provides important target region information for our active contour model. We subsequently introduce a dynamical shape model to provide prior target shape information for more stable segmentation. To obtain accurate target boundaries, we design a new multiple cues-based active contour model which integrates with target edge, discriminative region, and shape information. The experimental results on 30 video sequences demonstrate that the proposed method outperforms other competitive contour tracking methods under various tracking environment. Electronic supplementary materialThe online version of this article (

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Section: Related Workmentioning

confidence: 99%

Multiple cues-based active contours for target contour tracking under sophisticated background

Zhao

Chen

et al. 2016

Vis Comput

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“…However, they still can not entirely alleviate the problems confronted by the two types of matting methods. As such, much more methods are proposed for image/video matting [38], [39], [40], [41], [42]. More typically, the work [43] is based on the prior information of light field, and [44] is based on defocus spectral information.…”

mentioning

confidence: 99%

Patch alignment manifold matting

Li,

Liu,

Dong

et al. 2019

Preprint

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Image matting is generally modeled as a space transform from the color space to the alpha space. By estimating the alpha factor of the model, the foreground of an image can be extracted. However, there are some dimensional information redundancy in the alpha space. It usually leads to the misjudgments of some pixels near the boundary between the foreground and background. In this paper, a manifold matting framework named Patch Alignment Manifold Matting (PAMM) is proposed for image matting. In particular, we first propose a part modeling of color space in the local image patch. We then perform whole alignment optimization for approximating the alpha results by using subspace reconstructing error. Furthermore, we utilize the Nesterov's algorithm to solve the optimization problem. Finally, we apply some manifold learning methods in the framework, and obtain several image matting methods, such as named ISOMAP matting and its derived Cascade ISOMAP matting (CasISO matting). The experimental results reveal that the manifold matting framework and its two examples are effective when compared with several representative matting methods.

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“…Here, the first two terms measure the color fitness and texture penalty and the third term measures the compatibility of alpha in the current frame with the previously esti- Zou et al [70] proposed a video matting method that builds on the sparse coding method proposed in chapters 3 and 4 by adding a low-rank constraint to the sparse coding problem. A dictionary learning step is also added to increase the discriminative ability of the foreground and background samples.…”

Section: Temporally Coherent and Spatially Accurate Video Mattingmentioning

confidence: 99%

“…Two measures for temporal coherency are used by existing methods to evaluate matte quality. The first is the ratio of temporal derivatives of the matte and image frames introduced by Lee et al[53] and later used by recent methods[65,67,70].…”

mentioning

confidence: 99%

Sampling-based image and video matting without compositing equation

Johnson¹

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Video Matting via Sparse and Low-Rank Representation

Cited by 12 publications

References 28 publications

Multiple cues-based active contours for target contour tracking under sophisticated background

Multiple cues-based active contours for target contour tracking under sophisticated background

Patch alignment manifold matting

Sampling-based image and video matting without compositing equation

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