Ultra-High Resolution Image Segmentation via Locality-Aware Context Fusion and Alternating Local Enhancement

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local se...

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Bibliographic Details
Published inInternational journal of computer vision Vol. 132; no. 11; pp. 5030 - 5047
Main Authors Liu, Wenxi, Li, Qi, Lin, Xindai, Yang, Weixiang, He, Shengfeng, Yu, Yuanlong
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2024
Springer Nature B.V
Subjects
Artificial Intelligence
Computer Imaging
Computer Science
Context
High resolution
Image enhancement
Image Processing and Computer Vision
Image resolution
Image segmentation
Modules
Pattern Recognition
Pattern Recognition and Graphics
Semantics
Vision
Attention mechanism
Ultra-high resolution image segmentation
Context-guided vision model
Geo-spatial image segmentation
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Summary:Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present the alternating local enhancement module that restricts the negative impact of redundant information introduced from the contexts, and thus is endowed with the ability of fixing the locality-aware features to produce refined results. Furthermore, in comprehensive experiments, we demonstrate that our model outperforms other state-of-the-art methods in public benchmarks and verify the effectiveness of the proposed modules. Our released codes will be available at: https://github.com/liqiokkk/FCtL .
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-024-02045-3