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Dense Semantic Refinement Using Active Similarity Learning

Connor Clarkson, Mike Edwards Orcid Logo, Xianghua Xie Orcid Logo

International Journal on Computer Science and Information Systems, Volume: 19, Issue: 1

Swansea University Authors: Connor Clarkson, Mike Edwards Orcid Logo, Xianghua Xie Orcid Logo

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Abstract

Defect detection has achieved state-of-the-art results in both localisation and classification of various types of defects, manufacturing domains is no exception to this. Just like in many areas of computer vision there is an assume of very high-quality datasets that have been verified by domain exp...

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Published in: International Journal on Computer Science and Information Systems
ISSN: 1646-3642
Published: IADIS 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66967
first_indexed 2024-07-05T08:31:48Z
last_indexed 2025-09-18T07:16:24Z
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spelling 2025-09-17T17:15:35.2832567 v2 66967 2024-07-05 Dense Semantic Refinement Using Active Similarity Learning e1a00716a3866cd4d8bb0ade1bada119 Connor Clarkson Connor Clarkson true false 684864a1ce01c3d774e83ed55e41770e 0000-0003-3367-969X Mike Edwards Mike Edwards true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2024-07-05 MACS Defect detection has achieved state-of-the-art results in both localisation and classification of various types of defects, manufacturing domains is no exception to this. Just like in many areas of computer vision there is an assume of very high-quality datasets that have been verified by domain experts, however labelling such data has become an increasing problem as we require greater quantities of it. Within defect detection the variability and composite nature of defect characteristics makes this a time-consuming and interactionheavy task with great amount of expert effort. We propose a new acquisition function based on the similarity of defect properties for refining labels over time by showing the expert only the most required to be labelled. We also explore different ways in which the expert labels defects and how we should feed these new refinements back into the model for utilising new knowledge in an effortful way. We achieve this with a graphical interface that provides additional information as data gets refined into a dense segmentation, allowing for decision-making with uncertain areas of the image. Journal Article International Journal on Computer Science and Information Systems 19 1 IADIS 1646-3642 Similarity Learning, Data Refinement, Active Learning, Defect Detection, Interactive, AcquisitionFunction 24 6 2024 2024-06-24 https://www.iadisportal.org/ijcsis/ COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Funded by the EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems (EP/S021892/1) 2025-09-17T17:15:35.2832567 2024-07-05T09:28:45.1700937 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Connor Clarkson 1 Mike Edwards 0000-0003-3367-969X 2 Xianghua Xie 0000-0002-2701-8660 3
title Dense Semantic Refinement Using Active Similarity Learning
spellingShingle Dense Semantic Refinement Using Active Similarity Learning
Connor Clarkson
Mike Edwards
Xianghua Xie
title_short Dense Semantic Refinement Using Active Similarity Learning
title_full Dense Semantic Refinement Using Active Similarity Learning
title_fullStr Dense Semantic Refinement Using Active Similarity Learning
title_full_unstemmed Dense Semantic Refinement Using Active Similarity Learning
title_sort Dense Semantic Refinement Using Active Similarity Learning
author_id_str_mv e1a00716a3866cd4d8bb0ade1bada119
684864a1ce01c3d774e83ed55e41770e
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv e1a00716a3866cd4d8bb0ade1bada119_***_Connor Clarkson
684864a1ce01c3d774e83ed55e41770e_***_Mike Edwards
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Connor Clarkson
Mike Edwards
Xianghua Xie
author2 Connor Clarkson
Mike Edwards
Xianghua Xie
format Journal article
container_title International Journal on Computer Science and Information Systems
container_volume 19
container_issue 1
publishDate 2024
institution Swansea University
issn 1646-3642
publisher IADIS
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url https://www.iadisportal.org/ijcsis/
document_store_str 0
active_str 0
description Defect detection has achieved state-of-the-art results in both localisation and classification of various types of defects, manufacturing domains is no exception to this. Just like in many areas of computer vision there is an assume of very high-quality datasets that have been verified by domain experts, however labelling such data has become an increasing problem as we require greater quantities of it. Within defect detection the variability and composite nature of defect characteristics makes this a time-consuming and interactionheavy task with great amount of expert effort. We propose a new acquisition function based on the similarity of defect properties for refining labels over time by showing the expert only the most required to be labelled. We also explore different ways in which the expert labels defects and how we should feed these new refinements back into the model for utilising new knowledge in an effortful way. We achieve this with a graphical interface that provides additional information as data gets refined into a dense segmentation, allowing for decision-making with uncertain areas of the image.
published_date 2024-06-24T05:21:41Z
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score 11.089386

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