Next Article in Journal
Verifying the Effectiveness of New Face Spoofing DB with Capture Angle and Distance
Next Article in Special Issue
Combustion Instability Monitoring through Deep-Learning-Based Classification of Sequential High-Speed Flame Images
Previous Article in Journal
Findings about LORETA Applied to High-Density EEG—A Review
Previous Article in Special Issue
Feasibility Analysis of Deep Learning-Based Reality Assessment of Human Back-View Images
Open AccessArticle

Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning

1
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
2
Key Lab of Intelligent Computing and Information Security in Universities of Shandong, Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Institute of Biomedical Sciences, Shandong Normal University, Jinan 250358, China
3
School of Light Industry Science and Engineering, Qilu University of Technology, Jinan 250351, China
4
Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031, China
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(4), 659; https://doi.org/10.3390/electronics9040659
Received: 19 February 2020 / Revised: 9 April 2020 / Accepted: 13 April 2020 / Published: 17 April 2020
(This article belongs to the Special Issue Deep Learning Based Object Detection)
Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive. In this paper, a two-step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks), which are employed as the input to generate a binary mask. One-shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to perform the binary mask prediction task. The mask is further used to predict the trimap using image dilation. Extensive experiments were performed to evaluate the proposed algorithm. Experimental results show that the trimaps generated using the proposed algorithm are visually similar to the user-annotated ones. Comparing with the interactive matting algorithms, the proposed algoritm is less labor-intensive than trimap-based matting algorithm and achieved more accuate results than scribble-based matting algorithm. View Full-Text
Keywords: trimap; image matting; deep learning; one-shot learning; image segmentation trimap; image matting; deep learning; one-shot learning; image segmentation
Show Figures

Figure 1

MDPI and ACS Style

Chen, Z.; Zheng, Y.; Li, X.; Luo, R.; Jia, W.; Lian, J.; Li, C. Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning. Electronics 2020, 9, 659.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop