Learning-Based Sampling for Natural Image Matting

Tang, Jingwei; Aksoy, Yağız; Öztireli, Cengiz; Groß, Markus; Aydın, Tunç Ozan

doi:10.1109/cvpr.2019.00317

Cited by 113 publications

(97 citation statements)

References 16 publications

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“…Xu et al [31] proposed a large-scale data set neural network model, which mainly solves the problem of using only low-level features and lacking high-level context semantic information. Tang et al [34] proposed a method based on a mixture of sampling and learning. Before opacity estimation, a deep neural network was used to estimate the layer colour, which greatly improved the performance of opacity estimation.…”

Section: Matting Algorithmmentioning

confidence: 99%

Consistent image processing based on co‐saliency

Ren

Hua

et al. 2021

CAAI Trans on Intel Tech

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In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co-saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co-saliency detection. Through the co-saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non-common saliency objects (called Region of Interest here, i.e. ROI) in the image group can be obtained. In the attention retargeting algorithm, the internal features of the image to adjust the saliency of the ROI areas are considered. In the HSI colour space, the three components H, S, and I are adjusted separately. First, the hue distribution is constructed by the Dirac kernel function, and then the most similar hue distribution to the surrounding environment is selected as the best hue distribution of ROI areas. The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands. Experimental results show that this method effectively reduces the ROI areas' attraction to the user's visual attention. Moreover, comparing this method with other methods, the saliency adjustment effect achieved is much better, and the processed image is more natural. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Matting Algorithmmentioning

confidence: 99%

Consistent image processing based on co‐saliency

Ren

Hua

et al. 2021

CAAI Trans on Intel Tech

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“…Many deep learning-based alpha matting methods have been proposed recently. Some of them require only photos as input [2,3], while others require photos and trimaps [1,4,5,6,7].…”

Section: Alpha Matting and Trimap Generationmentioning

confidence: 99%

Automatic Trimap Generation by a Multimodal Neural Network

Takeuchi¹,

Tezuka²

2021

2021 IEEE International Conference on Image Processing (ICIP)

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In many of the existing alpha matting implementations, an intermediate representation called a trimap needs to be created manually. To automate the process, we propose a generic neural network for trimap generation based on saliency map detection. Our model multi-modally learns a saliency map and a trimap simultaneously. Because of this structure, the network focuses on reducing the error of the trimap especially within the areas with high salience. We used both the saliency map detection dataset and the alpha matting dataset to achieve accuracy in extracting subjects from natural images and generating trimaps. Experiments showed that our model could generate trimaps that are almost identical to manually generated ones. The method can also be easily combined with existing alpha matting algorithms.

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“…AlphaGAN [13] applies generative adversarial networks (GANs) to the image matting task for more realistic and sharper results. Tang et al [14] propose a hybrid sampling-based and learning-based matting method, which estimates the background and foreground color values for unknown regions as guidance in predicting alpha matte values.…”

Section: Image Mattingmentioning

confidence: 99%

JMNet: A joint matting network for automatic human matting

Fang

Chen³

et al. 2020

Comp. Visual Media

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We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images. We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module, which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques; the shared encoder leads to better performance and lower memory costs. Our model can process real images downloaded from the Internet for use in composition applications.

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Learning-Based Sampling for Natural Image Matting

Cited by 113 publications

References 16 publications

Consistent image processing based on co‐saliency

Consistent image processing based on co‐saliency

Automatic Trimap Generation by a Multimodal Neural Network

JMNet: A joint matting network for automatic human matting

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