Graph Based Temporal Aggregation for Video Retrieval
Large scale video retrieval is a field of study with a lot of ongoing research. Most of the work in the field is on video retrieval through text queries using techniques such as VSE++. However, there is little research done on video retrieval through image queries, and the work that has been done in...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Srinivasan, Arvind Bharadwaj, Aprameya Saha, Aveek Natarajan, Subramanyam |
| description | Large scale video retrieval is a field of study with a lot of ongoing
research. Most of the work in the field is on video retrieval through text
queries using techniques such as VSE++. However, there is little research done
on video retrieval through image queries, and the work that has been done in
this field either uses image queries from within the video dataset or iterates
through videos frame by frame. These approaches are not generalized for queries
from outside the dataset and do not scale well for large video datasets. To
overcome these issues, we propose a new approach for video retrieval through
image queries where an undirected graph is constructed from the combined set of
frames from all videos to be searched. The node features of this graph are used
in the task of video retrieval. Experimentation is done on the MSR-VTT dataset
by using query images from outside the dataset. To evaluate this novel approach
P@5, P@10 and P@20 metrics are calculated. Two different ResNet models namely,
ResNet-152 and ResNet-50 are used in this study. |
| doi_str_mv | 10.48550/arxiv.2011.02426 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2011_02426</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2011_02426</sourcerecordid><originalsourceid>FETCH-LOGICAL-a676-ba19e0777568613d7360433c7bd80c07fbdf12c5d75908cc9366f336edd031793</originalsourceid><addsrcrecordid>eNotzsFKw0AQgOG9eJDqA3hyXyBxNpPdSY61aBUKBQlew2R3Ni6kJmxL0bcXq6f_9vMpdWegrBtr4YHzVzqXFRhTQlVX7lrV28zLh37kowTdyWGZM096PY5ZRj6l-VPHOev3FGTWb3LKSc483airyNNRbv-7Ut3zU7d5KXb77etmvSvYkSsGNq0AEVnXOIOB0EGN6GkIDXigOIRoKm8D2RYa71t0LiI6CQHQUIsrdf-3vbD7JacD5-_-l99f-PgD13o-Gw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Graph Based Temporal Aggregation for Video Retrieval</title><source>arXiv.org</source><creator>Srinivasan, Arvind ; Bharadwaj, Aprameya ; Saha, Aveek ; Natarajan, Subramanyam</creator><creatorcontrib>Srinivasan, Arvind ; Bharadwaj, Aprameya ; Saha, Aveek ; Natarajan, Subramanyam</creatorcontrib><description>Large scale video retrieval is a field of study with a lot of ongoing
research. Most of the work in the field is on video retrieval through text
queries using techniques such as VSE++. However, there is little research done
on video retrieval through image queries, and the work that has been done in
this field either uses image queries from within the video dataset or iterates
through videos frame by frame. These approaches are not generalized for queries
from outside the dataset and do not scale well for large video datasets. To
overcome these issues, we propose a new approach for video retrieval through
image queries where an undirected graph is constructed from the combined set of
frames from all videos to be searched. The node features of this graph are used
in the task of video retrieval. Experimentation is done on the MSR-VTT dataset
by using query images from outside the dataset. To evaluate this novel approach
P@5, P@10 and P@20 metrics are calculated. Two different ResNet models namely,
ResNet-152 and ResNet-50 are used in this study.</description><identifier>DOI: 10.48550/arxiv.2011.02426</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Information Retrieval ; Computer Science - Learning</subject><creationdate>2020-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2011.02426$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2011.02426$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Srinivasan, Arvind</creatorcontrib><creatorcontrib>Bharadwaj, Aprameya</creatorcontrib><creatorcontrib>Saha, Aveek</creatorcontrib><creatorcontrib>Natarajan, Subramanyam</creatorcontrib><title>Graph Based Temporal Aggregation for Video Retrieval</title><description>Large scale video retrieval is a field of study with a lot of ongoing
research. Most of the work in the field is on video retrieval through text
queries using techniques such as VSE++. However, there is little research done
on video retrieval through image queries, and the work that has been done in
this field either uses image queries from within the video dataset or iterates
through videos frame by frame. These approaches are not generalized for queries
from outside the dataset and do not scale well for large video datasets. To
overcome these issues, we propose a new approach for video retrieval through
image queries where an undirected graph is constructed from the combined set of
frames from all videos to be searched. The node features of this graph are used
in the task of video retrieval. Experimentation is done on the MSR-VTT dataset
by using query images from outside the dataset. To evaluate this novel approach
P@5, P@10 and P@20 metrics are calculated. Two different ResNet models namely,
ResNet-152 and ResNet-50 are used in this study.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Information Retrieval</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzsFKw0AQgOG9eJDqA3hyXyBxNpPdSY61aBUKBQlew2R3Ni6kJmxL0bcXq6f_9vMpdWegrBtr4YHzVzqXFRhTQlVX7lrV28zLh37kowTdyWGZM096PY5ZRj6l-VPHOev3FGTWb3LKSc483airyNNRbv-7Ut3zU7d5KXb77etmvSvYkSsGNq0AEVnXOIOB0EGN6GkIDXigOIRoKm8D2RYa71t0LiI6CQHQUIsrdf-3vbD7JacD5-_-l99f-PgD13o-Gw</recordid><startdate>20201104</startdate><enddate>20201104</enddate><creator>Srinivasan, Arvind</creator><creator>Bharadwaj, Aprameya</creator><creator>Saha, Aveek</creator><creator>Natarajan, Subramanyam</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20201104</creationdate><title>Graph Based Temporal Aggregation for Video Retrieval</title><author>Srinivasan, Arvind ; Bharadwaj, Aprameya ; Saha, Aveek ; Natarajan, Subramanyam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-ba19e0777568613d7360433c7bd80c07fbdf12c5d75908cc9366f336edd031793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Information Retrieval</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Srinivasan, Arvind</creatorcontrib><creatorcontrib>Bharadwaj, Aprameya</creatorcontrib><creatorcontrib>Saha, Aveek</creatorcontrib><creatorcontrib>Natarajan, Subramanyam</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Srinivasan, Arvind</au><au>Bharadwaj, Aprameya</au><au>Saha, Aveek</au><au>Natarajan, Subramanyam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Graph Based Temporal Aggregation for Video Retrieval</atitle><date>2020-11-04</date><risdate>2020</risdate><abstract>Large scale video retrieval is a field of study with a lot of ongoing
research. Most of the work in the field is on video retrieval through text
queries using techniques such as VSE++. However, there is little research done
on video retrieval through image queries, and the work that has been done in
this field either uses image queries from within the video dataset or iterates
through videos frame by frame. These approaches are not generalized for queries
from outside the dataset and do not scale well for large video datasets. To
overcome these issues, we propose a new approach for video retrieval through
image queries where an undirected graph is constructed from the combined set of
frames from all videos to be searched. The node features of this graph are used
in the task of video retrieval. Experimentation is done on the MSR-VTT dataset
by using query images from outside the dataset. To evaluate this novel approach
P@5, P@10 and P@20 metrics are calculated. Two different ResNet models namely,
ResNet-152 and ResNet-50 are used in this study.</abstract><doi>10.48550/arxiv.2011.02426</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2011.02426 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2011_02426 |
| source | arXiv.org |
| subjects | Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval Computer Science - Learning |
| title | Graph Based Temporal Aggregation for Video Retrieval |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T09%3A13%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Graph%20Based%20Temporal%20Aggregation%20for%20Video%20Retrieval&rft.au=Srinivasan,%20Arvind&rft.date=2020-11-04&rft_id=info:doi/10.48550/arxiv.2011.02426&rft_dat=%3Carxiv_GOX%3E2011_02426%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |