Explainable Image Recognition With Graph-Based Feature Extraction Article Swipe
Basim Azam
,
Deepthi Kuttichira
,
Brijesh Verma
,
Ashfaqur Rahman
,
Lipo Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3475380
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3475380
Deep learning models have proven remarkably adept at extracting salient features from raw data, driving state-of-the-art performance across many domains. However, these models suffer from a lack of interpretability; they function as black boxes, obscuring the feature-level support of their predictions. Addressing this problem, we introduce a novel framework that combines the strengths of convolutional layers in extracting features with the adaptability of Graph Neural Networks (GNNs) to effectively represent the interconnections among neuron activations. Our framework operates in two phases: first, it identifies class-oriented neuron activations by analyzing image features, then these activations are encapsulated within a graph structure. The GNN in our system utilizes the connections between neuron activations to yield an interpretable final classification. This approach allows for the backtracking of predictions to identify key contributing neurons, enhancing the model's explainability. The proposed model not only matches, but at times exceeds, the accuracy of current leading models, all the while providing transparency via class-specific feature importance. This novel integration of convolutional and graph neural networks offers a significant step towards interpretable and accountable deep learning models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3475380
- OA Status
- gold
- Cited By
- 5
- References
- 26
- Related Works
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- OpenAlex ID
- https://openalex.org/W4403182677
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403182677Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3475380Digital Object Identifier
- Title
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Explainable Image Recognition With Graph-Based Feature ExtractionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
Basim Azam, Deepthi Kuttichira, Brijesh Verma, Ashfaqur Rahman, Lipo WangList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3475380Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2024.3475380Direct OA link when available
- Concepts
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Computer science, Feature extraction, Artificial intelligence, Pattern recognition (psychology), Graph, Feature (linguistics), Computer vision, Theoretical computer science, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5Per-year citation counts (last 5 years)
- References (count)
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26Number 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.raw | 12 |
| abstract_inverted_index.the | 35, 51, 60, 70, 106, 121, 131, 144, 151 |
| abstract_inverted_index.two | 79 |
| abstract_inverted_index.via | 155 |
| abstract_inverted_index.Deep | 0 |
| abstract_inverted_index.This | 117, 159 |
| abstract_inverted_index.deep | 176 |
| abstract_inverted_index.from | 11, 24 |
| abstract_inverted_index.have | 3 |
| abstract_inverted_index.lack | 26 |
| abstract_inverted_index.many | 18 |
| abstract_inverted_index.only | 138 |
| abstract_inverted_index.step | 171 |
| abstract_inverted_index.that | 49 |
| abstract_inverted_index.then | 91 |
| abstract_inverted_index.they | 29 |
| abstract_inverted_index.this | 42 |
| abstract_inverted_index.with | 59 |
| abstract_inverted_index.Graph | 63 |
| abstract_inverted_index.adept | 6 |
| abstract_inverted_index.among | 72 |
| abstract_inverted_index.black | 32 |
| abstract_inverted_index.data, | 13 |
| abstract_inverted_index.final | 115 |
| abstract_inverted_index.graph | 98, 165 |
| abstract_inverted_index.image | 89 |
| abstract_inverted_index.model | 136 |
| abstract_inverted_index.novel | 47, 160 |
| abstract_inverted_index.their | 39 |
| abstract_inverted_index.these | 21, 92 |
| abstract_inverted_index.times | 142 |
| abstract_inverted_index.while | 152 |
| abstract_inverted_index.yield | 112 |
| abstract_inverted_index.(GNNs) | 66 |
| abstract_inverted_index.Neural | 64 |
| abstract_inverted_index.across | 17 |
| abstract_inverted_index.allows | 119 |
| abstract_inverted_index.boxes, | 33 |
| abstract_inverted_index.first, | 81 |
| abstract_inverted_index.layers | 55 |
| abstract_inverted_index.models | 2, 22 |
| abstract_inverted_index.neural | 166 |
| abstract_inverted_index.neuron | 73, 85, 109 |
| abstract_inverted_index.offers | 168 |
| abstract_inverted_index.proven | 4 |
| abstract_inverted_index.suffer | 23 |
| abstract_inverted_index.system | 104 |
| abstract_inverted_index.within | 96 |
| abstract_inverted_index.between | 108 |
| abstract_inverted_index.current | 147 |
| abstract_inverted_index.driving | 14 |
| abstract_inverted_index.feature | 157 |
| abstract_inverted_index.leading | 148 |
| abstract_inverted_index.model's | 132 |
| abstract_inverted_index.models, | 149 |
| abstract_inverted_index.models. | 178 |
| abstract_inverted_index.phases: | 80 |
| abstract_inverted_index.salient | 9 |
| abstract_inverted_index.support | 37 |
| abstract_inverted_index.towards | 172 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.Networks | 65 |
| abstract_inverted_index.accuracy | 145 |
| abstract_inverted_index.approach | 118 |
| abstract_inverted_index.combines | 50 |
| abstract_inverted_index.domains. | 19 |
| abstract_inverted_index.exceeds, | 143 |
| abstract_inverted_index.features | 10, 58 |
| abstract_inverted_index.function | 30 |
| abstract_inverted_index.identify | 126 |
| abstract_inverted_index.learning | 1, 177 |
| abstract_inverted_index.matches, | 139 |
| abstract_inverted_index.networks | 167 |
| abstract_inverted_index.neurons, | 129 |
| abstract_inverted_index.operates | 77 |
| abstract_inverted_index.problem, | 43 |
| abstract_inverted_index.proposed | 135 |
| abstract_inverted_index.utilizes | 105 |
| abstract_inverted_index.analyzing | 88 |
| abstract_inverted_index.enhancing | 130 |
| abstract_inverted_index.features, | 90 |
| abstract_inverted_index.framework | 48, 76 |
| abstract_inverted_index.introduce | 45 |
| abstract_inverted_index.obscuring | 34 |
| abstract_inverted_index.providing | 153 |
| abstract_inverted_index.represent | 69 |
| abstract_inverted_index.strengths | 52 |
| abstract_inverted_index.Addressing | 41 |
| abstract_inverted_index.extracting | 8, 57 |
| abstract_inverted_index.identifies | 83 |
| abstract_inverted_index.remarkably | 5 |
| abstract_inverted_index.structure. | 99 |
| abstract_inverted_index.accountable | 175 |
| abstract_inverted_index.activations | 86, 93, 110 |
| abstract_inverted_index.connections | 107 |
| abstract_inverted_index.effectively | 68 |
| abstract_inverted_index.importance. | 158 |
| abstract_inverted_index.integration | 161 |
| abstract_inverted_index.performance | 16 |
| abstract_inverted_index.predictions | 124 |
| abstract_inverted_index.significant | 170 |
| abstract_inverted_index.activations. | 74 |
| abstract_inverted_index.adaptability | 61 |
| abstract_inverted_index.backtracking | 122 |
| abstract_inverted_index.contributing | 128 |
| abstract_inverted_index.encapsulated | 95 |
| abstract_inverted_index.predictions. | 40 |
| abstract_inverted_index.transparency | 154 |
| abstract_inverted_index.convolutional | 54, 163 |
| abstract_inverted_index.feature-level | 36 |
| abstract_inverted_index.interpretable | 114, 173 |
| abstract_inverted_index.class-oriented | 84 |
| abstract_inverted_index.class-specific | 156 |
| abstract_inverted_index.classification. | 116 |
| abstract_inverted_index.explainability. | 133 |
| abstract_inverted_index.interconnections | 71 |
| abstract_inverted_index.state-of-the-art | 15 |
| abstract_inverted_index.interpretability; | 28 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.86312823 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |