Statistical Evaluation of Video Summarization Models from an Empirical Perspective Article Swipe
Ankit Kumar
,
Saroj Kumar Pandey
,
Chetan Swarup
,
Kamred Udham Singh
,
Teekam Singh
,
Manoj Kumar Ojha
,
Pankaj Mishra
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18280/ts.400214
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18280/ts.400214
Video summarization is the process of creating a shortened version of a longer video while retaining its essential content and meaning.It entails automatically identifying the most important parts of the video and selecting the relevant frames, shots, or scenes that best represent the original video's content.Video summarization entails complex image signal analysis and processing to extract the most important frames or shots from a video while discarding redundant or less informative ones.Several stages of analysis and processing are typically involved in the process, which may include video segmentation, feature extraction, frame selection, classification, and quality assessment.A variety of algorithms and system models are available for this task.Classification architectures such as convolutional neural networks, recurrent neural networks, and others are used to categorize video frames as redundant or non-redundant.This article provides a categorization and analysis of video summarization methodologies, with a focus on methods from the real-time video summarizing (RVS) domain.The current study will aid in laying the groundwork for future research and investigating potential research avenues by combining key research findings and data for quick reference.Video summarization has been shown to be useful in a variety of real-world contexts in smart cities, such as detecting anomalies in a video surveillance system.To address this issue, research studies can be conducted to evaluate and compare different video summarization algorithms in terms of their effectiveness, efficiency, and suitability for various applications.These studies can use benchmark datasets and standardized evaluation metrics to provide objective and quantitative comparisons of different algorithms.Based on the findings of these studies, researchers and multimedia system designers can make informed decisions about which algorithmic combination will work best for their application.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18280/ts.400214
- https://www.iieta.org/download/file/fid/97765
- OA Status
- bronze
- References
- 39
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3206421219Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18280/ts.400214Digital Object Identifier
- Title
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Statistical Evaluation of Video Summarization Models from an Empirical PerspectiveWork title
- Type
-
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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2023-04-30Full publication date if available
- Authors
-
Ankit Kumar, Saroj Kumar Pandey, Chetan Swarup, Kamred Udham Singh, Teekam Singh, Manoj Kumar Ojha, Pankaj MishraList of authors in order
- Landing page
-
https://doi.org/10.18280/ts.400214Publisher landing page
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https://www.iieta.org/download/file/fid/97765Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://www.iieta.org/download/file/fid/97765Direct OA link when available
- Concepts
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Automatic summarization, Perspective (graphical), Computer science, Empirical research, Artificial intelligence, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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39Number 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.in | 80, 154, 183, 189, 196, 217 |
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| abstract_inverted_index.and | 19, 31, 52, 75, 93, 99, 116, 132, 161, 171, 211, 223, 233, 240, 253 |
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| abstract_inverted_index.use | 230 |
| abstract_inverted_index.been | 178 |
| abstract_inverted_index.best | 40, 267 |
| abstract_inverted_index.data | 172 |
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| abstract_inverted_index.with | 138 |
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| abstract_inverted_index.(RVS) | 148 |
| abstract_inverted_index.Video | 0 |
| abstract_inverted_index.about | 261 |
| abstract_inverted_index.focus | 140 |
| abstract_inverted_index.frame | 90 |
| abstract_inverted_index.image | 49 |
| abstract_inverted_index.parts | 27 |
| abstract_inverted_index.quick | 174 |
| abstract_inverted_index.shots | 61 |
| abstract_inverted_index.shown | 179 |
| abstract_inverted_index.smart | 190 |
| abstract_inverted_index.study | 151 |
| abstract_inverted_index.terms | 218 |
| abstract_inverted_index.their | 220, 269 |
| abstract_inverted_index.these | 250 |
| abstract_inverted_index.video | 13, 30, 64, 86, 122, 135, 146, 198, 214 |
| abstract_inverted_index.which | 83, 262 |
| abstract_inverted_index.while | 14, 65 |
| abstract_inverted_index.frames | 59, 123 |
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| abstract_inverted_index.issue, | 203 |
| abstract_inverted_index.laying | 155 |
| abstract_inverted_index.longer | 12 |
| abstract_inverted_index.models | 101 |
| abstract_inverted_index.neural | 111, 114 |
| abstract_inverted_index.others | 117 |
| abstract_inverted_index.scenes | 38 |
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| abstract_inverted_index.system | 100, 255 |
| abstract_inverted_index.useful | 182 |
| abstract_inverted_index.address | 201 |
| abstract_inverted_index.article | 128 |
| abstract_inverted_index.avenues | 165 |
| abstract_inverted_index.cities, | 191 |
| abstract_inverted_index.compare | 212 |
| abstract_inverted_index.complex | 48 |
| abstract_inverted_index.content | 18 |
| abstract_inverted_index.current | 150 |
| abstract_inverted_index.entails | 21, 47 |
| abstract_inverted_index.extract | 55 |
| abstract_inverted_index.feature | 88 |
| abstract_inverted_index.frames, | 35 |
| abstract_inverted_index.include | 85 |
| abstract_inverted_index.methods | 142 |
| abstract_inverted_index.metrics | 236 |
| abstract_inverted_index.process | 4 |
| abstract_inverted_index.provide | 238 |
| abstract_inverted_index.quality | 94 |
| abstract_inverted_index.studies | 205, 228 |
| abstract_inverted_index.variety | 96, 185 |
| abstract_inverted_index.various | 226 |
| abstract_inverted_index.version | 9 |
| abstract_inverted_index.video's | 44 |
| abstract_inverted_index.analysis | 51, 74, 133 |
| abstract_inverted_index.contexts | 188 |
| abstract_inverted_index.creating | 6 |
| abstract_inverted_index.datasets | 232 |
| abstract_inverted_index.evaluate | 210 |
| abstract_inverted_index.findings | 170, 248 |
| abstract_inverted_index.informed | 259 |
| abstract_inverted_index.involved | 79 |
| abstract_inverted_index.original | 43 |
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| abstract_inverted_index.research | 160, 164, 169, 204 |
| abstract_inverted_index.studies, | 251 |
| abstract_inverted_index.anomalies | 195 |
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| abstract_inverted_index.benchmark | 231 |
| abstract_inverted_index.combining | 167 |
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| abstract_inverted_index.decisions | 260 |
| abstract_inverted_index.designers | 256 |
| abstract_inverted_index.detecting | 194 |
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| abstract_inverted_index.important | 26, 58 |
| abstract_inverted_index.networks, | 112, 115 |
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| abstract_inverted_index.potential | 163 |
| abstract_inverted_index.real-time | 145 |
| abstract_inverted_index.recurrent | 113 |
| abstract_inverted_index.redundant | 67, 125 |
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| abstract_inverted_index.retaining | 15 |
| abstract_inverted_index.selecting | 32 |
| abstract_inverted_index.shortened | 8 |
| abstract_inverted_index.system.To | 200 |
| abstract_inverted_index.typically | 78 |
| abstract_inverted_index.algorithms | 98, 216 |
| abstract_inverted_index.categorize | 121 |
| abstract_inverted_index.discarding | 66 |
| abstract_inverted_index.domain.The | 149 |
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| abstract_inverted_index.groundwork | 157 |
| abstract_inverted_index.meaning.It | 20 |
| abstract_inverted_index.multimedia | 254 |
| abstract_inverted_index.processing | 53, 76 |
| abstract_inverted_index.real-world | 187 |
| abstract_inverted_index.selection, | 91 |
| abstract_inverted_index.algorithmic | 263 |
| abstract_inverted_index.combination | 264 |
| abstract_inverted_index.comparisons | 242 |
| abstract_inverted_index.efficiency, | 222 |
| abstract_inverted_index.extraction, | 89 |
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| abstract_inverted_index.researchers | 252 |
| abstract_inverted_index.suitability | 224 |
| abstract_inverted_index.summarizing | 147 |
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| abstract_inverted_index.assessment.A | 95 |
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| abstract_inverted_index.standardized | 234 |
| abstract_inverted_index.surveillance | 199 |
| abstract_inverted_index.architectures | 107 |
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| abstract_inverted_index.investigating | 162 |
| abstract_inverted_index.segmentation, | 87 |
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| abstract_inverted_index.categorization | 131 |
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| abstract_inverted_index.methodologies, | 137 |
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| abstract_inverted_index.algorithms.Based | 245 |
| abstract_inverted_index.applications.These | 227 |
| abstract_inverted_index.non-redundant.This | 127 |
| abstract_inverted_index.task.Classification | 106 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5029027974 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I120238654 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.00167998 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |