Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation Article Swipe
Ranju Mandal
,
Basim Azam
,
Brijesh Verma
,
Jun Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/ssci51031.2022.10022103
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/ssci51031.2022.10022103
Image segmentation is one of the major challenges in real-world computer vision applications. Context-embedded network models proposed for image segmentation have outperformed context-free models. However, optimized values of many parameters need to consider for such a complex network. The manual parameter selection process is ineffective and produces suboptimal performance for such a model. Therefore, we propose a context-based genetically optimized network model for image segmentation in this paper. Genetic algorithms enhance the performance of the deep network model by determining the best parameter values. The proposed three-level deep network is adaptable to image context by extracting visual and context-rich features and optimally integrating them to obtain final pixel labels for scene images. The genetic algorithm ensures optimal parameter values in all three levels to obtain a globally optimized network model to achieve the best segmentation results.
Related Topics
Concepts
Computer science
Context (archaeology)
Artificial intelligence
Image segmentation
Genetic algorithm
Segmentation
Artificial neural network
Image (mathematics)
Context model
Process (computing)
Segmentation-based object categorization
Pixel
Pattern recognition (psychology)
Scale-space segmentation
Selection (genetic algorithm)
Machine learning
Computer vision
Biology
Object (grammar)
Paleontology
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ssci51031.2022.10022103
- OA Status
- green
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4318606405
All OpenAlex metadata
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https://openalex.org/W4318606405Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ssci51031.2022.10022103Digital Object Identifier
- Title
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Genetic Algorithms for Optimising Context-based Neural Networks for Image SegmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-04Full publication date if available
- Authors
-
Ranju Mandal, Basim Azam, Brijesh Verma, Jun ZhangList of authors in order
- Landing page
-
https://doi.org/10.1109/ssci51031.2022.10022103Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10072/422709Direct OA link when available
- Concepts
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Computer science, Context (archaeology), Artificial intelligence, Image segmentation, Genetic algorithm, Segmentation, Artificial neural network, Image (mathematics), Context model, Process (computing), Segmentation-based object categorization, Pixel, Pattern recognition (psychology), Scale-space segmentation, Selection (genetic algorithm), Machine learning, Computer vision, Biology, Object (grammar), Paleontology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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45Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | 2022 IEEE Symposium Series on Computational Intelligence (SSCI) |
| primary_location.landing_page_url | https://doi.org/10.1109/ssci51031.2022.10022103 |
| publication_date | 2022-12-04 |
| publication_year | 2022 |
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