Improving sampling-based image matting with cooperative coevolution differential evolution algorithm

Cai, Zixing; Lv, Liang; Huang, Han; Huang, Hui; Liang, Yihui

doi:10.1007/s00500-016-2250-7

Cited by 25 publications

(12 citation statements)

References 35 publications

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“…Although PMF provides heuristic information across the multi-scale pixel pair search, MOEAMCD-PMF exacerbates the imbalance reducing the quality of identified pixel pairs, because additional VOLUME 8, 2020 FIGURE 5. Visual comparison of alpha mattes obtained by MOEAMCD-PMF, MOEAMCD [10], KL divergence matting [22] PDMS matting [15] and CCDE matting [23]. (a) Input image in which the red line and the blue line denote the boundary of known foreground regions and background regions respectively.…”

Section: Tablementioning

confidence: 99%

“…(g) Alpha matte obtained by PDMS matting [15]. (h) Alpha matte obtained by CCDE matting [23]. Arrows indicate regions of low-quality mattes.…”

Section: Tablementioning

confidence: 99%

“…Recently, several researchers [10], [23]- [25] demonstrated that pixel pairs can be optimized without sampling by using the evolutionary algorithm (a population-based metaheuristic optimization algorithm [26]), which can make use of all available computing resources by adjusting the iteration number of the evolutionary algorithm, and would in theory eliminate the risk of true sample omission. In these approaches (which are known as evolutionary-optimizationbased approaches), all PPO problems involved in an image are solved simultaneously by combining them into one large-scale PPO problem, as illustrated in Eq.…”

Section: Introductionmentioning

confidence: 99%

“…The evolutionary-optimization-based approach improves the performance of image matting by performing a global search with an evolutionary algorithm. However, the evolutionary-optimization-based approach requires extensive computing resources (hundreds of thousands PPE/UP [23]) to provide a high-quality alpha matte. Therefore, some attempts have been made to improve the search capability of evolutionary-optimization-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, some attempts have been made to improve the search capability of evolutionary-optimization-based approaches. Cai et al [23] suggested that correlations of unknown pixel colors can be used to improve the search capability of evolutionary-optimization-based approaches. In their work, a cooperative coevolution operator was embedded into differential evolution algorithm to group unknown pixels with similar colors and optimize the groups separately.…”

Section: Introductionmentioning

confidence: 99%

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Pyramid Matting: A Resource-Adaptive Multi-Scale Pixel Pair Optimization Framework for Image Matting

2020

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Image matting is an important problem in computer vision with significant theoretical interest and diverse practical applications, including image/video editing, target tracking, and object recognition. Pixel-pair-optimization-based image matting approaches have been shown very successful in estimating the opacity of the foreground by searching for the best pair of foreground and background pixels for each unknown pixel. However, extant approaches encounter difficulties in adapting to the changes of available computing resources, which limits the application of image matting. This drawback has motivated the present study, as a part of which a multi-scale evolutionary pixel pair optimization framework named pyramid matting framework (PMF) was developed. In this framework, the large-scale pixel pair optimization problem is transformed to multiple pixel pair optimization problems of different scales using image pyramid. The resulting problems are solved level by level, starting from the problem at the small scale. Pixel pair heuristic information obtained from solving low-scale problems are iteratively propagated to the spatially-related pixel pairs in the larger-scale problem. PMF can adapt to changes in available computing resources due to its capability of transforming a small-scale problem solution to the large-scale problem solution through the heuristic information propagation. Experimental results show that the PMF-based image matting approach not only provides high-quality alpha mattes with sufficient computing resources, but also works well when computing resources are scarce.

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