Fully Convolutional Neural Network with Relation Aware Context Information for Image Parsing Article Swipe
Basim Azam
,
Ranju Mandal
,
Brijesh Verma
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/dicta52665.2021.9647219
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/dicta52665.2021.9647219
Image parsing is among the core tasks in the field of computer vision. The automatic pixel-wise segmentation offers great potential in terms of application adaptability. Traditional convolutional networks have produced better segmentation maps however the research is continued for integration of context information with neural network approaches. In this paper, we propose an image parsing framework that explores the traditional convolutions in fully convolutional networks and learns rich semantic contextual information using the adjacent and spatial modules to generate probability maps. The implicit fusion of the probability maps generated enhances the accuracy of segmentation labels. The proposed framework improves the segmentation accuracy on the CamVid dataset achieving global accuracy of 89.8 %. A comprehensive comparison with state-of-the-art approaches demonstrates that the proposed network exhibits the capability to adapt to the dataset specific information and has the potential to outperform cutting-edge segmentation models.
Related Topics
Concepts
Computer science
Parsing
Artificial intelligence
Segmentation
Convolutional neural network
Context (archaeology)
Image segmentation
Pattern recognition (psychology)
Relation (database)
Adaptability
Pixel
Conditional random field
Enhanced Data Rates for GSM Evolution
Machine learning
Computer vision
Data mining
Biology
Paleontology
Ecology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/dicta52665.2021.9647219
- OA Status
- green
- Cited By
- 1
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200198046
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200198046Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/dicta52665.2021.9647219Digital Object Identifier
- Title
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Fully Convolutional Neural Network with Relation Aware Context Information for Image ParsingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-11-01Full publication date if available
- Authors
-
Basim Azam, Ranju Mandal, Brijesh VermaList of authors in order
- Landing page
-
https://doi.org/10.1109/dicta52665.2021.9647219Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10072/422710Direct OA link when available
- Concepts
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Computer science, Parsing, Artificial intelligence, Segmentation, Convolutional neural network, Context (archaeology), Image segmentation, Pattern recognition (psychology), Relation (database), Adaptability, Pixel, Conditional random field, Enhanced Data Rates for GSM Evolution, Machine learning, Computer vision, Data mining, Biology, Paleontology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 4, 8, 34, 58, 72, 85, 90, 99, 103, 120, 124, 129, 135 |
| abstract_inverted_index.89.8 | 110 |
| abstract_inverted_index.core | 5 |
| abstract_inverted_index.have | 28 |
| abstract_inverted_index.maps | 32, 87 |
| abstract_inverted_index.rich | 67 |
| abstract_inverted_index.that | 56, 119 |
| abstract_inverted_index.this | 48 |
| abstract_inverted_index.with | 43, 115 |
| abstract_inverted_index.Image | 0 |
| abstract_inverted_index.adapt | 127 |
| abstract_inverted_index.among | 3 |
| abstract_inverted_index.field | 9 |
| abstract_inverted_index.fully | 62 |
| abstract_inverted_index.great | 18 |
| abstract_inverted_index.image | 53 |
| abstract_inverted_index.maps. | 80 |
| abstract_inverted_index.tasks | 6 |
| abstract_inverted_index.terms | 21 |
| abstract_inverted_index.using | 71 |
| abstract_inverted_index.CamVid | 104 |
| abstract_inverted_index.better | 30 |
| abstract_inverted_index.fusion | 83 |
| abstract_inverted_index.global | 107 |
| abstract_inverted_index.learns | 66 |
| abstract_inverted_index.neural | 44 |
| abstract_inverted_index.offers | 17 |
| abstract_inverted_index.paper, | 49 |
| abstract_inverted_index.context | 41 |
| abstract_inverted_index.dataset | 105, 130 |
| abstract_inverted_index.however | 33 |
| abstract_inverted_index.labels. | 94 |
| abstract_inverted_index.models. | 141 |
| abstract_inverted_index.modules | 76 |
| abstract_inverted_index.network | 45, 122 |
| abstract_inverted_index.parsing | 1, 54 |
| abstract_inverted_index.propose | 51 |
| abstract_inverted_index.spatial | 75 |
| abstract_inverted_index.vision. | 12 |
| abstract_inverted_index.accuracy | 91, 101, 108 |
| abstract_inverted_index.adjacent | 73 |
| abstract_inverted_index.computer | 11 |
| abstract_inverted_index.enhances | 89 |
| abstract_inverted_index.exhibits | 123 |
| abstract_inverted_index.explores | 57 |
| abstract_inverted_index.generate | 78 |
| abstract_inverted_index.implicit | 82 |
| abstract_inverted_index.improves | 98 |
| abstract_inverted_index.networks | 27, 64 |
| abstract_inverted_index.produced | 29 |
| abstract_inverted_index.proposed | 96, 121 |
| abstract_inverted_index.research | 35 |
| abstract_inverted_index.semantic | 68 |
| abstract_inverted_index.specific | 131 |
| abstract_inverted_index.achieving | 106 |
| abstract_inverted_index.automatic | 14 |
| abstract_inverted_index.continued | 37 |
| abstract_inverted_index.framework | 55, 97 |
| abstract_inverted_index.generated | 88 |
| abstract_inverted_index.potential | 19, 136 |
| abstract_inverted_index.approaches | 117 |
| abstract_inverted_index.capability | 125 |
| abstract_inverted_index.comparison | 114 |
| abstract_inverted_index.contextual | 69 |
| abstract_inverted_index.outperform | 138 |
| abstract_inverted_index.pixel-wise | 15 |
| abstract_inverted_index.Traditional | 25 |
| abstract_inverted_index.application | 23 |
| abstract_inverted_index.approaches. | 46 |
| abstract_inverted_index.information | 42, 70, 132 |
| abstract_inverted_index.integration | 39 |
| abstract_inverted_index.probability | 79, 86 |
| abstract_inverted_index.traditional | 59 |
| abstract_inverted_index.convolutions | 60 |
| abstract_inverted_index.cutting-edge | 139 |
| abstract_inverted_index.demonstrates | 118 |
| abstract_inverted_index.segmentation | 16, 31, 93, 100, 140 |
| abstract_inverted_index.adaptability. | 24 |
| abstract_inverted_index.comprehensive | 113 |
| abstract_inverted_index.convolutional | 26, 63 |
| abstract_inverted_index.state-of-the-art | 116 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.43637722 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |