The VISIONE Video Search System : Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval
This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:7 |
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| Enthalten in: |
Journal of imaging - 7(2021), 5 vom: 23. Apr. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Amato, Giuseppe [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 03.09.2021 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/jimaging7050076 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM330034375 |
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| 100 | 1 | |a Amato, Giuseppe |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The VISIONE Video Search System |b Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval |
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| 500 | |a Date Revised 03.09.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Ad-hoc video search | |
| 650 | 4 | |a content-based video retrieval | |
| 650 | 4 | |a image search | |
| 650 | 4 | |a information systems applications | |
| 650 | 4 | |a known item search | |
| 650 | 4 | |a multimedia and multimodal retrieval | |
| 650 | 4 | |a multimedia information systems | |
| 650 | 4 | |a retrieval models and ranking | |
| 650 | 4 | |a surrogate text representation | |
| 650 | 4 | |a users and interactive retrieval | |
| 650 | 4 | |a video search | |
| 700 | 1 | |a Bolettieri, Paolo |e verfasserin |4 aut | |
| 700 | 1 | |a Carrara, Fabio |e verfasserin |4 aut | |
| 700 | 1 | |a Debole, Franca |e verfasserin |4 aut | |
| 700 | 1 | |a Falchi, Fabrizio |e verfasserin |4 aut | |
| 700 | 1 | |a Gennaro, Claudio |e verfasserin |4 aut | |
| 700 | 1 | |a Vadicamo, Lucia |e verfasserin |4 aut | |
| 700 | 1 | |a Vairo, Claudio |e verfasserin |4 aut | |
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