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CLC number: TP39
On-line Access: 2023-03-25
Received: 2022-03-15
Revision Accepted: 2023-03-25
Crosschecked: 2022-08-31
Cited: 0
Clicked: 1160
UI layers merger: merging UI layers via visual learning and boundary prior
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Yunnong CHEN, Yankun ZHEN, Chuning SHI, Jiazhi LI, Liuqing CHEN, Zejian LI, Lingyun SUN, Tingting ZHOU, Yanfang CHANG. UI layers merger: merging UI layers via visual learning and boundary prior[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(3): 373-387.
@article{title="UI layers merger: merging UI layers via visual learning and boundary prior",
author="Yunnong CHEN, Yankun ZHEN, Chuning SHI, Jiazhi LI, Liuqing CHEN, Zejian LI, Lingyun SUN, Tingting ZHOU, Yanfang CHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="3",
pages="373-387",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200099"
}
%0 Journal Article
%T UI layers merger: merging UI layers via visual learning and boundary prior
%A Yunnong CHEN
%A Yankun ZHEN
%A Chuning SHI
%A Jiazhi LI
%A Liuqing CHEN
%A Zejian LI
%A Lingyun SUN
%A Tingting ZHOU
%A Yanfang CHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 3
%P 373-387
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200099
TY - JOUR
T1 - UI layers merger: merging UI layers via visual learning and boundary prior
A1 - Yunnong CHEN
A1 - Yankun ZHEN
A1 - Chuning SHI
A1 - Jiazhi LI
A1 - Liuqing CHEN
A1 - Zejian LI
A1 - Lingyun SUN
A1 - Tingting ZHOU
A1 - Yanfang CHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 3
SP - 373
EP - 387
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200099
Abstract: With the fast-growing graphical user interface (GUI) development workload in the Internet industry, some work attempted to generate maintainable front-end code from GUI screenshots. It can be more suitable for using user interface (UI) design drafts that contain UI metadata. However, fragmented layers inevitably appear in the UI design drafts, which greatly reduces the quality of the generated code. None of the existing automated GUI techniques detects and merges the fragmented layers to improve the accessibility of generated code. In this paper, we propose UI layers merger (UILM), a vision-based method that can automatically detect and merge fragmented layers into UI components. Our UILM contains the merging area detector (MAD) and a layer merging algorithm. The MAD incorporates the boundary prior knowledge to accurately detect the boundaries of UI components. Then, the layer merging algorithm can search for the associated layers within the components' boundaries and merge them into a whole. We present a dynamic data augmentation approach to boost the performance of MAD. We also construct a large-scale UI dataset for training the MAD and testing the performance of UILM. Experimental results show that the proposed method outperforms the best baseline regarding merging area detection and achieves decent layer merging accuracy. A user study on a real application also confirms the effectiveness of our UILM.
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