Image and Video Captioning Using Machine Learning Article Swipe
Dhruv Kumar
,
Dhawal Kamble
,
Sayuja Kute
,
Narayan Chavan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22214/ijraset.2023.56796
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22214/ijraset.2023.56796
Image based web crawler is the way toward looking through data by utilizing related images. The tremendous assets of images are accessible on the web in that a large number of the images are contain as with named and without named caption. The users are needed to look through the images relying upon their necessities. In that a significant number of the users can't recover the pertinent images as a result of their unpredicted appropriate inscription on their images. Our task is to generate an automatic caption for the images based on the image content. To produce an image caption, firstly, the content of the image should be fully understood; and then the semantic information contained in the image should be described using a phrase or statement that conforms to certain grammatical rules. Thus, it requires techniques from both computer vision and natural language processing to connect the two different media forms together, which is highly challenging. The paper targets producing mechanized inscriptions by learning the contents of the image. At present images are clarified with human intercession and it turns out to be almost unthinkable task for tremendous databases. The picture information base is given as contribution to a deep neural network Convolutional Neural Network encoder for creating caption which extricates the highlights and subtleties out of our image and Recurrent Neural Network decoder is utilized to interpret the highlights and articles given by our image to acquire consecutive, meaningful description of the image.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.22214/ijraset.2023.56796
- https://doi.org/10.22214/ijraset.2023.56796
- OA Status
- diamond
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389870419
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389870419Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22214/ijraset.2023.56796Digital Object Identifier
- Title
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Image and Video Captioning Using Machine LearningWork title
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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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2023-12-17Full publication date if available
- Authors
-
Dhruv Kumar, Dhawal Kamble, Sayuja Kute, Narayan ChavanList of authors in order
- Landing page
-
https://doi.org/10.22214/ijraset.2023.56796Publisher landing page
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https://doi.org/10.22214/ijraset.2023.56796Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://doi.org/10.22214/ijraset.2023.56796Direct OA link when available
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Closed captioning, Computer science, Task (project management), Artificial intelligence, Convolutional neural network, Image (mathematics), Phrase, Computer vision, Natural language processing, Information retrieval, Economics, ManagementTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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15Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.users | 44, 63 |
| abstract_inverted_index.using | 123 |
| abstract_inverted_index.which | 154, 211 |
| abstract_inverted_index.Neural | 205, 223 |
| abstract_inverted_index.almost | 185 |
| abstract_inverted_index.assets | 18 |
| abstract_inverted_index.highly | 156 |
| abstract_inverted_index.image. | 170, 245 |
| abstract_inverted_index.images | 20, 33, 51, 68, 90, 173 |
| abstract_inverted_index.needed | 46 |
| abstract_inverted_index.neural | 202 |
| abstract_inverted_index.number | 30, 60 |
| abstract_inverted_index.phrase | 125 |
| abstract_inverted_index.result | 71 |
| abstract_inverted_index.rules. | 133 |
| abstract_inverted_index.should | 107, 120 |
| abstract_inverted_index.toward | 8 |
| abstract_inverted_index.vision | 141 |
| abstract_inverted_index.Network | 206, 224 |
| abstract_inverted_index.acquire | 239 |
| abstract_inverted_index.caption | 87, 210 |
| abstract_inverted_index.certain | 131 |
| abstract_inverted_index.connect | 147 |
| abstract_inverted_index.contain | 35 |
| abstract_inverted_index.content | 103 |
| abstract_inverted_index.crawler | 4 |
| abstract_inverted_index.decoder | 225 |
| abstract_inverted_index.encoder | 207 |
| abstract_inverted_index.images. | 15, 79 |
| abstract_inverted_index.looking | 9 |
| abstract_inverted_index.natural | 143 |
| abstract_inverted_index.network | 203 |
| abstract_inverted_index.picture | 192 |
| abstract_inverted_index.present | 172 |
| abstract_inverted_index.produce | 97 |
| abstract_inverted_index.recover | 65 |
| abstract_inverted_index.related | 14 |
| abstract_inverted_index.relying | 52 |
| abstract_inverted_index.targets | 160 |
| abstract_inverted_index.through | 10, 49 |
| abstract_inverted_index.without | 40 |
| abstract_inverted_index.articles | 233 |
| abstract_inverted_index.caption, | 100 |
| abstract_inverted_index.caption. | 42 |
| abstract_inverted_index.computer | 140 |
| abstract_inverted_index.conforms | 129 |
| abstract_inverted_index.content. | 95 |
| abstract_inverted_index.contents | 167 |
| abstract_inverted_index.creating | 209 |
| abstract_inverted_index.firstly, | 101 |
| abstract_inverted_index.generate | 84 |
| abstract_inverted_index.language | 144 |
| abstract_inverted_index.learning | 165 |
| abstract_inverted_index.requires | 136 |
| abstract_inverted_index.semantic | 114 |
| abstract_inverted_index.utilized | 227 |
| abstract_inverted_index.Abstract: | 0 |
| abstract_inverted_index.Recurrent | 222 |
| abstract_inverted_index.automatic | 86 |
| abstract_inverted_index.clarified | 175 |
| abstract_inverted_index.contained | 116 |
| abstract_inverted_index.described | 122 |
| abstract_inverted_index.different | 150 |
| abstract_inverted_index.interpret | 229 |
| abstract_inverted_index.pertinent | 67 |
| abstract_inverted_index.producing | 161 |
| abstract_inverted_index.statement | 127 |
| abstract_inverted_index.together, | 153 |
| abstract_inverted_index.utilizing | 13 |
| abstract_inverted_index.accessible | 22 |
| abstract_inverted_index.databases. | 190 |
| abstract_inverted_index.extricates | 212 |
| abstract_inverted_index.highlights | 214, 231 |
| abstract_inverted_index.meaningful | 241 |
| abstract_inverted_index.mechanized | 162 |
| abstract_inverted_index.processing | 145 |
| abstract_inverted_index.subtleties | 216 |
| abstract_inverted_index.techniques | 137 |
| abstract_inverted_index.tremendous | 17, 189 |
| abstract_inverted_index.appropriate | 75 |
| abstract_inverted_index.description | 242 |
| abstract_inverted_index.grammatical | 132 |
| abstract_inverted_index.information | 115, 193 |
| abstract_inverted_index.inscription | 76 |
| abstract_inverted_index.significant | 59 |
| abstract_inverted_index.understood; | 110 |
| abstract_inverted_index.unpredicted | 74 |
| abstract_inverted_index.unthinkable | 186 |
| abstract_inverted_index.challenging. | 157 |
| abstract_inverted_index.consecutive, | 240 |
| abstract_inverted_index.contribution | 198 |
| abstract_inverted_index.inscriptions | 163 |
| abstract_inverted_index.intercession | 178 |
| abstract_inverted_index.necessities. | 55 |
| abstract_inverted_index.Convolutional | 204 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.2040169 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |