GAT-Based Bi-CARU with Adaptive Feature-Based Transformation for Video Summarisation Article Swipe
Ka‐Hou Chan
,
Sio‐Kei Im
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/technologies12080126
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/technologies12080126
Nowadays, video is a common social media in our lives. Video summarisation has become an interesting task for information extraction, where the challenge of high redundancy of key scenes leads to difficulties in retrieving important messages. To address this challenge, this work presents a novel approach called the Graph Attention (GAT)-based bi-directional content-adaptive recurrent unit model for video summarisation. The model makes use of the graph attention approach to transform the visual features of interesting scene(s) from a video. This transformation is achieved by a mechanism called Adaptive Feature-based Transformation (AFT), which extracts the visual features and elevates them to a higher-level representation. We also introduce a new GAT-based attention model that extracts major features from weight features for information extraction, taking into account the tendency of humans to pay attention to transformations and moving objects. Additionally, we integrate the higher-level visual features obtained from the attention layer with the semantic features processed by Bi-CARU. By combining both visual and semantic information, the proposed work enhances the accuracy of key-scene determination. By addressing the issue of high redundancy among major information and using advanced techniques, our method provides a competitive and efficient way to summarise videos. Experimental results show that our approach outperforms existing state-of-the-art methods in video summarisation.
Related Topics
Concepts
Computer science
Redundancy (engineering)
Graph
Feature extraction
Artificial intelligence
Semantic gap
Visualization
Key (lock)
Information retrieval
Transformation (genetics)
Pattern recognition (psychology)
Image (mathematics)
Image retrieval
Theoretical computer science
Gene
Biochemistry
Chemistry
Computer security
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/technologies12080126
- https://www.mdpi.com/2227-7080/12/8/126/pdf?version=1722849488
- OA Status
- gold
- Cited By
- 3
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401328363
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401328363Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/technologies12080126Digital Object Identifier
- Title
-
GAT-Based Bi-CARU with Adaptive Feature-Based Transformation for Video SummarisationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-05Full publication date if available
- Authors
-
Ka‐Hou Chan, Sio‐Kei ImList of authors in order
- Landing page
-
https://doi.org/10.3390/technologies12080126Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-7080/12/8/126/pdf?version=1722849488Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7080/12/8/126/pdf?version=1722849488Direct OA link when available
- Concepts
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Computer science, Redundancy (engineering), Graph, Feature extraction, Artificial intelligence, Semantic gap, Visualization, Key (lock), Information retrieval, Transformation (genetics), Pattern recognition (psychology), Image (mathematics), Image retrieval, Theoretical computer science, Gene, Biochemistry, Chemistry, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3147852756, https://openalex.org/W2964167369, https://openalex.org/W3146366485, https://openalex.org/W3212386989, https://openalex.org/W4283399854, https://openalex.org/W2963919999, https://openalex.org/W3008767742, https://openalex.org/W2776189519, https://openalex.org/W2967038491, https://openalex.org/W2997333136, https://openalex.org/W4229458270, https://openalex.org/W3087792975, https://openalex.org/W4324355347, https://openalex.org/W4387968052, https://openalex.org/W3007364240, https://openalex.org/W4214661601, https://openalex.org/W3213412677, https://openalex.org/W2911286998, https://openalex.org/W3128443161, https://openalex.org/W3004820227, https://openalex.org/W4312658498, https://openalex.org/W4221138654, https://openalex.org/W4390873718, https://openalex.org/W3167393794, https://openalex.org/W4311461310, https://openalex.org/W3035820072, https://openalex.org/W2788303226, https://openalex.org/W4312748990, https://openalex.org/W4385626864, https://openalex.org/W3203003533, https://openalex.org/W4312508181, https://openalex.org/W4381233075, https://openalex.org/W2998841681, https://openalex.org/W4200497606, https://openalex.org/W3107283985, https://openalex.org/W3176472363, https://openalex.org/W3114523470, https://openalex.org/W3202774381, https://openalex.org/W3034815696, https://openalex.org/W6640109428, https://openalex.org/W2529272619, https://openalex.org/W2791384572, https://openalex.org/W2964158702, https://openalex.org/W2781922022, https://openalex.org/W4285600262, https://openalex.org/W4229444697, https://openalex.org/W3090254005, https://openalex.org/W4225769600, https://openalex.org/W2563296158 |
| referenced_works_count | 49 |
| abstract_inverted_index.a | 3, 43, 77, 84, 100, 106, 188 |
| abstract_inverted_index.By | 155, 171 |
| abstract_inverted_index.To | 36 |
| abstract_inverted_index.We | 103 |
| abstract_inverted_index.an | 14 |
| abstract_inverted_index.by | 83, 153 |
| abstract_inverted_index.in | 7, 32, 206 |
| abstract_inverted_index.is | 2, 81 |
| abstract_inverted_index.of | 23, 26, 63, 73, 126, 168, 175 |
| abstract_inverted_index.to | 30, 68, 99, 128, 131, 193 |
| abstract_inverted_index.we | 137 |
| abstract_inverted_index.The | 59 |
| abstract_inverted_index.and | 96, 133, 159, 181, 190 |
| abstract_inverted_index.for | 17, 56, 118 |
| abstract_inverted_index.has | 12 |
| abstract_inverted_index.key | 27 |
| abstract_inverted_index.new | 107 |
| abstract_inverted_index.our | 8, 185, 200 |
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| abstract_inverted_index.the | 21, 47, 64, 70, 93, 124, 139, 145, 149, 162, 166, 173 |
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| abstract_inverted_index.This | 79 |
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| abstract_inverted_index.high | 24, 176 |
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| abstract_inverted_index.show | 198 |
| abstract_inverted_index.task | 16 |
| abstract_inverted_index.that | 111, 199 |
| abstract_inverted_index.them | 98 |
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| abstract_inverted_index.with | 148 |
| abstract_inverted_index.work | 41, 164 |
| abstract_inverted_index.Graph | 48 |
| abstract_inverted_index.Video | 10 |
| abstract_inverted_index.among | 178 |
| abstract_inverted_index.graph | 65 |
| abstract_inverted_index.issue | 174 |
| abstract_inverted_index.layer | 147 |
| abstract_inverted_index.leads | 29 |
| abstract_inverted_index.major | 113, 179 |
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| abstract_inverted_index.media | 6 |
| abstract_inverted_index.model | 55, 60, 110 |
| abstract_inverted_index.novel | 44 |
| abstract_inverted_index.using | 182 |
| abstract_inverted_index.video | 1, 57, 207 |
| abstract_inverted_index.where | 20 |
| abstract_inverted_index.which | 91 |
| abstract_inverted_index.(AFT), | 90 |
| abstract_inverted_index.become | 13 |
| abstract_inverted_index.called | 46, 86 |
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| abstract_inverted_index.humans | 127 |
| abstract_inverted_index.lives. | 9 |
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| abstract_inverted_index.scenes | 28 |
| abstract_inverted_index.social | 5 |
| abstract_inverted_index.taking | 121 |
| abstract_inverted_index.video. | 78 |
| abstract_inverted_index.visual | 71, 94, 141, 158 |
| abstract_inverted_index.weight | 116 |
| abstract_inverted_index.account | 123 |
| abstract_inverted_index.address | 37 |
| abstract_inverted_index.methods | 205 |
| abstract_inverted_index.results | 197 |
| abstract_inverted_index.videos. | 195 |
| abstract_inverted_index.Adaptive | 87 |
| abstract_inverted_index.Bi-CARU. | 154 |
| abstract_inverted_index.accuracy | 167 |
| abstract_inverted_index.achieved | 82 |
| abstract_inverted_index.advanced | 183 |
| abstract_inverted_index.approach | 45, 67, 201 |
| abstract_inverted_index.elevates | 97 |
| abstract_inverted_index.enhances | 165 |
| abstract_inverted_index.existing | 203 |
| abstract_inverted_index.extracts | 92, 112 |
| abstract_inverted_index.features | 72, 95, 114, 117, 142, 151 |
| abstract_inverted_index.objects. | 135 |
| abstract_inverted_index.obtained | 143 |
| abstract_inverted_index.presents | 42 |
| abstract_inverted_index.proposed | 163 |
| abstract_inverted_index.provides | 187 |
| abstract_inverted_index.scene(s) | 75 |
| abstract_inverted_index.semantic | 150, 160 |
| abstract_inverted_index.tendency | 125 |
| abstract_inverted_index.Attention | 49 |
| abstract_inverted_index.GAT-based | 108 |
| abstract_inverted_index.Nowadays, | 0 |
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| abstract_inverted_index.challenge | 22 |
| abstract_inverted_index.combining | 156 |
| abstract_inverted_index.efficient | 191 |
| abstract_inverted_index.important | 34 |
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| abstract_inverted_index.introduce | 105 |
| abstract_inverted_index.key-scene | 169 |
| abstract_inverted_index.mechanism | 85 |
| abstract_inverted_index.messages. | 35 |
| abstract_inverted_index.processed | 152 |
| abstract_inverted_index.recurrent | 53 |
| abstract_inverted_index.summarise | 194 |
| abstract_inverted_index.transform | 69 |
| abstract_inverted_index.addressing | 172 |
| abstract_inverted_index.challenge, | 39 |
| abstract_inverted_index.redundancy | 25, 177 |
| abstract_inverted_index.retrieving | 33 |
| abstract_inverted_index.(GAT)-based | 50 |
| abstract_inverted_index.competitive | 189 |
| abstract_inverted_index.extraction, | 19, 120 |
| abstract_inverted_index.information | 18, 119, 180 |
| abstract_inverted_index.interesting | 15, 74 |
| abstract_inverted_index.outperforms | 202 |
| abstract_inverted_index.techniques, | 184 |
| abstract_inverted_index.Experimental | 196 |
| abstract_inverted_index.difficulties | 31 |
| abstract_inverted_index.higher-level | 101, 140 |
| abstract_inverted_index.information, | 161 |
| abstract_inverted_index.Additionally, | 136 |
| abstract_inverted_index.Feature-based | 88 |
| abstract_inverted_index.summarisation | 11 |
| abstract_inverted_index.Transformation | 89 |
| abstract_inverted_index.bi-directional | 51 |
| abstract_inverted_index.determination. | 170 |
| abstract_inverted_index.summarisation. | 58, 208 |
| abstract_inverted_index.transformation | 80 |
| abstract_inverted_index.representation. | 102 |
| abstract_inverted_index.transformations | 132 |
| abstract_inverted_index.content-adaptive | 52 |
| abstract_inverted_index.state-of-the-art | 204 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5061396090 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I49835588 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.77951901 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |