Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis Article Swipe
B Sushma
,
P. Aparna
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3044759
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3044759
Conventional Wireless capsule endoscopy (WCE) video summary generation techniques apprehend an image by extracting hand crafted features, which are not essentially sufficient to encapsulate the semantic similarity of endoscopic images. Use of supervised methods for extraction of deep features from an image need an enormous amount of accurate labelled data for training process. To solve this, we use an unsupervised learning method to extract features using convolutional auto encoder. Furthermore, WCE images are classified into similar and dissimilar pairs using fixed threshold derived through large number of experiments. Finally, keyframe extraction method based on motion analysis is used to derive a structured summary of WCE video. Proposed method achieves an average F-measure of 91.1% with compression ratio of 83.12%. The results indicate that the proposed method is more efficient compared to existing WCE video summarization techniques.
Related Topics
Concepts
Automatic summarization
Computer science
Artificial intelligence
Feature extraction
Computer vision
Encoder
Deep learning
Wireless
Similarity (geometry)
Matching (statistics)
Pattern recognition (psychology)
Convolutional neural network
Image (mathematics)
Statistics
Telecommunications
Mathematics
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3044759
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09293302.pdf
- OA Status
- gold
- Cited By
- 25
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3112120603
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3112120603Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.3044759Digital Object Identifier
- Title
-
Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion AnalysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-14Full publication date if available
- Authors
-
B Sushma, P. AparnaList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3044759Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09293302.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/9312710/09293302.pdfDirect OA link when available
- Concepts
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Automatic summarization, Computer science, Artificial intelligence, Feature extraction, Computer vision, Encoder, Deep learning, Wireless, Similarity (geometry), Matching (statistics), Pattern recognition (psychology), Convolutional neural network, Image (mathematics), Statistics, Telecommunications, Mathematics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
25Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 3, 2023: 10, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 27, 31, 36, 46, 86, 103, 112, 117 |
| abstract_inverted_index.on | 93 |
| abstract_inverted_index.to | 22, 62, 98, 130 |
| abstract_inverted_index.we | 56 |
| abstract_inverted_index.The | 119 |
| abstract_inverted_index.Use | 30 |
| abstract_inverted_index.WCE | 70, 104, 132 |
| abstract_inverted_index.and | 76 |
| abstract_inverted_index.are | 18, 72 |
| abstract_inverted_index.for | 34, 50 |
| abstract_inverted_index.not | 19 |
| abstract_inverted_index.the | 24, 123 |
| abstract_inverted_index.use | 57 |
| abstract_inverted_index.auto | 67 |
| abstract_inverted_index.data | 49 |
| abstract_inverted_index.deep | 37 |
| abstract_inverted_index.from | 39 |
| abstract_inverted_index.hand | 14 |
| abstract_inverted_index.into | 74 |
| abstract_inverted_index.more | 127 |
| abstract_inverted_index.need | 42 |
| abstract_inverted_index.that | 122 |
| abstract_inverted_index.used | 97 |
| abstract_inverted_index.with | 114 |
| abstract_inverted_index.(WCE) | 4 |
| abstract_inverted_index.91.1% | 113 |
| abstract_inverted_index.based | 92 |
| abstract_inverted_index.fixed | 80 |
| abstract_inverted_index.image | 11, 41 |
| abstract_inverted_index.large | 84 |
| abstract_inverted_index.pairs | 78 |
| abstract_inverted_index.ratio | 116 |
| abstract_inverted_index.solve | 54 |
| abstract_inverted_index.this, | 55 |
| abstract_inverted_index.using | 65, 79 |
| abstract_inverted_index.video | 5, 133 |
| abstract_inverted_index.which | 17 |
| abstract_inverted_index.amount | 45 |
| abstract_inverted_index.derive | 99 |
| abstract_inverted_index.images | 71 |
| abstract_inverted_index.method | 61, 91, 107, 125 |
| abstract_inverted_index.motion | 94 |
| abstract_inverted_index.number | 85 |
| abstract_inverted_index.video. | 105 |
| abstract_inverted_index.83.12%. | 118 |
| abstract_inverted_index.average | 110 |
| abstract_inverted_index.capsule | 2 |
| abstract_inverted_index.crafted | 15 |
| abstract_inverted_index.derived | 82 |
| abstract_inverted_index.extract | 63 |
| abstract_inverted_index.images. | 29 |
| abstract_inverted_index.methods | 33 |
| abstract_inverted_index.results | 120 |
| abstract_inverted_index.similar | 75 |
| abstract_inverted_index.summary | 6, 102 |
| abstract_inverted_index.through | 83 |
| abstract_inverted_index.Finally, | 88 |
| abstract_inverted_index.Proposed | 106 |
| abstract_inverted_index.Wireless | 1 |
| abstract_inverted_index.accurate | 47 |
| abstract_inverted_index.achieves | 108 |
| abstract_inverted_index.analysis | 95 |
| abstract_inverted_index.compared | 129 |
| abstract_inverted_index.encoder. | 68 |
| abstract_inverted_index.enormous | 44 |
| abstract_inverted_index.existing | 131 |
| abstract_inverted_index.features | 38, 64 |
| abstract_inverted_index.indicate | 121 |
| abstract_inverted_index.keyframe | 89 |
| abstract_inverted_index.labelled | 48 |
| abstract_inverted_index.learning | 60 |
| abstract_inverted_index.process. | 52 |
| abstract_inverted_index.proposed | 124 |
| abstract_inverted_index.semantic | 25 |
| abstract_inverted_index.training | 51 |
| abstract_inverted_index.F-measure | 111 |
| abstract_inverted_index.apprehend | 9 |
| abstract_inverted_index.efficient | 128 |
| abstract_inverted_index.endoscopy | 3 |
| abstract_inverted_index.features, | 16 |
| abstract_inverted_index.threshold | 81 |
| abstract_inverted_index.classified | 73 |
| abstract_inverted_index.dissimilar | 77 |
| abstract_inverted_index.endoscopic | 28 |
| abstract_inverted_index.extracting | 13 |
| abstract_inverted_index.extraction | 35, 90 |
| abstract_inverted_index.generation | 7 |
| abstract_inverted_index.similarity | 26 |
| abstract_inverted_index.structured | 101 |
| abstract_inverted_index.sufficient | 21 |
| abstract_inverted_index.supervised | 32 |
| abstract_inverted_index.techniques | 8 |
| abstract_inverted_index.compression | 115 |
| abstract_inverted_index.encapsulate | 23 |
| abstract_inverted_index.essentially | 20 |
| abstract_inverted_index.techniques. | 135 |
| abstract_inverted_index.Conventional | 0 |
| abstract_inverted_index.Furthermore, | 69 |
| abstract_inverted_index.experiments. | 87 |
| abstract_inverted_index.unsupervised | 59 |
| abstract_inverted_index.convolutional | 66 |
| abstract_inverted_index.summarization | 134 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.89244463 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |