A Graph-based Context Learning Technique for Image Parsing Article Swipe
Basim Azam
,
Brijesh Verma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/ijcnn54540.2023.10191800
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/ijcnn54540.2023.10191800
The modern deep learning-based architectures have performed well for pixel-wise segmentation tasks. The consideration of context is of vital importance for generation of accurate semantic information. In this research, a deep learning-based image parsing framework is proposed that utilizes novel relation-aware context learning technique. The proposed technique explores the graph constructs from the training data to learn the co-occurring context associations of object category labels using the graph edge connections. The proposed graph-based context learning technique defines the scene specific relation-awareness among semantic object categories, e.g., the probability of sky, road and building to co-exist in a scene is high. The proposed image parsing architecture (including the novel graph-based context learning technique) is evaluated on the benchmark datasets. In addition, a comprehensive comparison with existing image parsing techniques is presented to establish the efficacy of the scene-graph generation. The in-depth investigation of graph generation is presented to demonstrate the improvement in pixel-wise labeling.
Related Topics
Concepts
Computer science
Parsing
Scene graph
Artificial intelligence
Graph
Relation (database)
Pixel
Segmentation
Image segmentation
Dependency grammar
Context model
Deep learning
Pattern recognition (psychology)
Natural language processing
Machine learning
Computer vision
Object (grammar)
Theoretical computer science
Data mining
Rendering (computer graphics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ijcnn54540.2023.10191800
- OA Status
- green
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385482776
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https://openalex.org/W4385482776Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ijcnn54540.2023.10191800Digital Object Identifier
- Title
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A Graph-based Context Learning Technique for Image ParsingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-18Full publication date if available
- Authors
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Basim Azam, Brijesh VermaList of authors in order
- Landing page
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https://doi.org/10.1109/ijcnn54540.2023.10191800Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10072/427562Direct OA link when available
- Concepts
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Computer science, Parsing, Scene graph, Artificial intelligence, Graph, Relation (database), Pixel, Segmentation, Image segmentation, Dependency grammar, Context model, Deep learning, Pattern recognition (psychology), Natural language processing, Machine learning, Computer vision, Object (grammar), Theoretical computer science, Data mining, Rendering (computer graphics)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2970543709, https://openalex.org/W2119943055, https://openalex.org/W2395611524, https://openalex.org/W2319083191, https://openalex.org/W6729856380, https://openalex.org/W2022508996, https://openalex.org/W2320281308, https://openalex.org/W3132455321, https://openalex.org/W2901249224, https://openalex.org/W6638480814, https://openalex.org/W2963881378, https://openalex.org/W2981689412, https://openalex.org/W3047385447, https://openalex.org/W2028277005, https://openalex.org/W2017313218, https://openalex.org/W1581592866, https://openalex.org/W2412782625, https://openalex.org/W1915480574, https://openalex.org/W3159837173, https://openalex.org/W3022184340, https://openalex.org/W3011933327, https://openalex.org/W3194677016, https://openalex.org/W3121675078, https://openalex.org/W2962730651, https://openalex.org/W3085685449, https://openalex.org/W2102605133, https://openalex.org/W2561196672, https://openalex.org/W2737258237, https://openalex.org/W3092223688, https://openalex.org/W3125803554, https://openalex.org/W3036385345, https://openalex.org/W3159640305, https://openalex.org/W2142834259, https://openalex.org/W2963166928, https://openalex.org/W4200237038, https://openalex.org/W6696085341, https://openalex.org/W2753588254, https://openalex.org/W2461677039, https://openalex.org/W3082097505, https://openalex.org/W4210476769, https://openalex.org/W2037227137, https://openalex.org/W3177791215, https://openalex.org/W6666899075, https://openalex.org/W2737885667, https://openalex.org/W6717372056, https://openalex.org/W2911016673, https://openalex.org/W181701252, https://openalex.org/W3165745140, https://openalex.org/W4244914727, https://openalex.org/W2965391153, https://openalex.org/W1610707153, https://openalex.org/W6754123467, https://openalex.org/W4293406525, https://openalex.org/W2963840672, https://openalex.org/W1817277359, https://openalex.org/W2886934227, https://openalex.org/W2150609397, https://openalex.org/W2554423077, https://openalex.org/W4225608379, https://openalex.org/W2066813062, https://openalex.org/W4226283079, https://openalex.org/W3196670469 |
| referenced_works_count | 62 |
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| sustainable_development_goals[0].display_name | Sustainable cities and communities |
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| citation_normalized_percentile.is_in_top_10_percent | False |