NDLI: Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing

Content Provider	IET Digital Library
Author	Uhl, Johannes H. Leyk, Stefan Chiang, Yao Yi Duan, Weiwei Knoblock, Craig A.
Abstract	Convolutional neural networks (CNNs) such as encoder–decoder CNNs have increasingly been employed for semantic image segmentation at the pixel-level requiring pixel-level training labels, which are rarely available in real-world scenarios. In practice, weakly annotated training data at the image patch level are often used for pixel-level segmentation tasks, requiring further processing to obtain accurate results, mainly because the translation invariance of the CNN-based inference can turn into an impeding property leading to segmentation results of coarser spatial granularity compared with the original image. However, the inherent uncertainty in the segmented image and its relationships to translation invariance, CNN architecture, and classification scheme has never been analysed from an explicitly spatial perspective. Therefore, the authors propose measures to spatially visualise and assess class decision confidence based on spatially dense CNN predictions, resulting in continuous decision confidence surfaces. They find that such a visual-analytical method contributes to a better understanding of the spatial variability of class score confidence derived from weakly supervised CNN-based classifiers. They exemplify this approach by incorporating decision confidence surfaces into a processing chain for the extraction of human settlement features from historical map documents based on weakly annotated training data using different CNN architectures and classification schemes.
Starting Page	2084
Ending Page	2091
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 11, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5484
Journal	IET Image Processing
Publisher Date	2018-07-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cartography Class Decision Confidence Class Score Confidence Spatial Variability Classification Scheme CNN-based Inference Computer Vision And Image Processing Technique Continuous Decision Confidence Surfaces Data Visualisation Document Image Processing Document Processing Technique Encoder-decoder CNN Feature Extraction Feedforward Neural Network Geography And Cartography Computing Graphics Technique Historical Map Documents Historical Map Processing History Human Settlement Feature Extraction Image Classification Image Patch Level Image Recognition Image Resolution Image Segmentation Inference Mechanisms Knowledge Engineering Technique Neural Computing Technique Pixel-level Segmentation Task Pixel-level Training Labels Semantic Image Segmentation Spatial Granularity Spatialising Uncertainty Translation Invariance Visual-analytical Method Weakly Annotated Training Data Weakly Supervised CNN-based Classifier Weakly Supervised Convolutional Neural Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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