Deep Learning Model with GA based Feature Selection and Context Integration Article Swipe
Ranju Mandal
,
Basim Azam
,
Brijesh Verma
,
Jun Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.06189
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.06189
Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Many top-performing methods for image segmentation are based on deep CNN models. However, deep CNN models fail to integrate global and local context alongside visual features despite having complex multi-layer architectures. We propose a novel three-layered deep learning model that assiminlate or learns independently global and local contextual information alongside visual features. The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer that learns global and local contexts of an image, and finally the third layer integrates all the information optimally to obtain the final class label. Stanford Background and CamVid benchmark image parsing datasets were used for our model evaluation, and our model shows promising results. The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models.
Related Topics
Concepts
Computer science
Artificial intelligence
Deep learning
Context (archaeology)
Benchmark (surveying)
Feature (linguistics)
Layer (electronics)
Feature selection
Class (philosophy)
Pattern recognition (psychology)
Parsing
Machine learning
Segmentation
Philosophy
Biology
Geography
Linguistics
Geodesy
Paleontology
Organic chemistry
Chemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.06189
- https://arxiv.org/pdf/2204.06189
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224330736
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https://openalex.org/W4224330736Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2204.06189Digital Object Identifier
- Title
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Deep Learning Model with GA based Feature Selection and Context IntegrationWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-13Full publication date if available
- Authors
-
Ranju Mandal, Basim Azam, Brijesh Verma, Jun ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.06189Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2204.06189Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2204.06189Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Deep learning, Context (archaeology), Benchmark (surveying), Feature (linguistics), Layer (electronics), Feature selection, Class (philosophy), Pattern recognition (psychology), Parsing, Machine learning, Segmentation, Philosophy, Biology, Geography, Linguistics, Geodesy, Paleontology, Organic chemistry, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.propose | 49 |
| abstract_inverted_index.reveals | 147 |
| abstract_inverted_index.However, | 29 |
| abstract_inverted_index.Stanford | 124 |
| abstract_inverted_index.accuracy | 164 |
| abstract_inverted_index.analysis | 146 |
| abstract_inverted_index.computer | 8 |
| abstract_inverted_index.contexts | 104 |
| abstract_inverted_index.datasets | 131 |
| abstract_inverted_index.features | 42, 89, 151 |
| abstract_inverted_index.followed | 94 |
| abstract_inverted_index.learners | 80 |
| abstract_inverted_index.learning | 1, 54 |
| abstract_inverted_index.proposed | 73 |
| abstract_inverted_index.results. | 143 |
| abstract_inverted_index.Algorithm | 86 |
| abstract_inverted_index.alongside | 40, 66 |
| abstract_inverted_index.benchmark | 128 |
| abstract_inverted_index.empirical | 145 |
| abstract_inverted_index.features. | 68 |
| abstract_inverted_index.integrate | 35 |
| abstract_inverted_index.optimally | 117 |
| abstract_inverted_index.optimized | 88, 149 |
| abstract_inverted_index.promising | 142 |
| abstract_inverted_index.Background | 125 |
| abstract_inverted_index.One-vs-All | 77 |
| abstract_inverted_index.comparable | 169 |
| abstract_inverted_index.contextual | 64, 97, 156 |
| abstract_inverted_index.inception, | 16 |
| abstract_inverted_index.integrates | 113 |
| abstract_inverted_index.introduced | 82 |
| abstract_inverted_index.processing | 12 |
| abstract_inverted_index.successful | 6 |
| abstract_inverted_index.assiminlate | 57 |
| abstract_inverted_index.class-based | 79 |
| abstract_inverted_index.evaluation, | 137 |
| abstract_inverted_index.information | 65, 116, 157 |
| abstract_inverted_index.multi-layer | 46 |
| abstract_inverted_index.predictions | 168 |
| abstract_inverted_index.significant | 160 |
| abstract_inverted_index.segmentation | 22 |
| abstract_inverted_index.applications. | 13 |
| abstract_inverted_index.independently | 60 |
| abstract_inverted_index.three-layered | 52 |
| abstract_inverted_index.architectures. | 47 |
| abstract_inverted_index.top-performing | 18 |
| abstract_inverted_index.state-of-the-art | 171 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.02713871 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |