Pixel-Level Discrete Multiobjective Sampling for Image Matting

Huang, Han; Liang, Yihui; Yang, Xiaowei; Zhang, Hao

doi:10.1109/tip.2019.2902830

Cited by 21 publications

(14 citation statements)

References 45 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%

“…Considering the change of available computing resources in image matting applications, the PPO problem is required to be solved approximately with different amounts of available computing resources. The amount of available computing resources can be quantitatively described by the number of available pixel pair evaluations per unknown pixel (PPE/UP) [15]. The PPO-based approaches described in pertinent literature can be categorized into: sampling-based and evolutionary-optimization-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…The approaches based on local sampling require a small amount of computing resources (most of them require hundreds to thousands PPE/UP [14], [17]). However, local sampling strategies may miss the true sample when the best pair of pixels is not located on the boundary, degrading the alpha matte quality significantly [15], [18]- [22].…”

Section: Introductionmentioning

confidence: 99%

“…In their work, Kullback-Leibler divergence was employed to measure the feature distance among the cluster centers obtained by a superpixel approach, which provided a quantitative measurement of pixel sample dissimilarity. Huang et al [15] cautioned that true sample may be missed due to: (1) conflicts among sampling criteria adopted; and (2) an incomplete sample space coused by using pixel clustering approaches. Thus they modeled pixel sampling for each unknown pixel as a multi-objective optimization problem and selected the Pareto optimal pixels as samples.…”

Section: Introductionmentioning

confidence: 99%

“…In the multi-objective sampling, the pixel (rather than the superpixel) was used as the sampling unit. Global sampling strategies improve the diversity of pixel samples by expanding the sample space and designing sophisticated sampling strategy, while more computing resources are required (thousands to tens of thousands PPE/UP [15]) as the complexity of sampling strategy increases.…”

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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