VIRET at Video Browser Showdown 2020

Lokoč, Jakub; Kovalčík, Gregor; Souček, Tomáš

doi:10.1007/978-3-030-37734-2_70

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…SOMHunter [27] VIRET [38] vitrivr [58] VIREO [47] Exquisitor [25] IVIST [48] AAU [32] ITEC [32] VERGE [1] score…”

Section: Overview Of Methods Integrated To Compared Systemsmentioning

confidence: 99%

“…The search strategy for the VIRET system [38] consists of iterative query formulation and resulting frame set inspection (using thumbnails) with integrated video preview and summary browsing approaches. Selected frames and their features for text search and query by example image were the same as for SOMHunter (i.e., using the BoW variant of the W2VV++ model [33,35]).…”

Section: Viret (Charles University Czech Republic)mentioning

confidence: 99%

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Is the Reign of Interactive Search Eternal? Findings from the Video Browser Showdown 2020

Lokoč

Veselý

Mejzlík

et al. 2021

ACM Trans. Multimedia Comput. Commun. Appl.

Self Cite

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Comprehensive and fair performance evaluation of information retrieval systems represents an essential task for the current information age. Whereas Cranfield-based evaluations with benchmark datasets support development of retrieval models, significant evaluation efforts are required also for user-oriented systems that try to boost performance with an interactive search approach. This article presents findings from the 9th Video Browser Showdown, a competition that focuses on a legitimate comparison of interactive search systems designed for challenging known-item search tasks over a large video collection. During previous installments of the competition, the interactive nature of participating systems was a key feature to satisfy known-item search needs, and this article continues to support this hypothesis. Despite the fact that top-performing systems integrate the most recent deep learning models into their retrieval process, interactive searching remains a necessary component of successful strategies for known-item search tasks. Alongside the description of competition settings, evaluated tasks, participating teams, and overall results, this article presents a detailed analysis of query logs collected by the top three performing systems, SOMHunter, VIRET, and vitrivr. The analysis provides a quantitative insight to the observed performance of the systems and constitutes a new baseline methodology for future events. The results reveal that the top two systems mostly relied on temporal queries before a correct frame was identified. An interaction log analysis complements the result log findings and points to the importance of result set and video browsing approaches. Finally, various outlooks are discussed in order to improve the Video Browser Showdown challenge in the future.

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“…SOMHunter [27] VIRET [38] vitrivr [58] VIREO [47] Exquisitor [25] IVIST [48] AAU [32] ITEC [32] VERGE [1] score…”

Section: Overview Of Methods Integrated To Compared Systemsmentioning

confidence: 99%

Section: Viret (Charles University Czech Republic)mentioning

confidence: 99%

Is the Reign of Interactive Search Eternal? Findings from the Video Browser Showdown 2020

Lokoč

Veselý

Mejzlík

et al. 2021

ACM Trans. Multimedia Comput. Commun. Appl.

Self Cite

View full text Add to dashboard Cite

show abstract

“…VIRET [ 25 , 28 ] is an interactive frame-based video retrieval system that currently provides four main retrieval modules (query by keyword, query by free-form text, queries by color sketch, and query by example). The keyword search relies on automatic annotation of video keyframes.…”

Section: Related Workmentioning

confidence: 99%

The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

et al. 2021

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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.

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“…Additionally, users could choose relevant frames for relevance feedback and they could repeat this process until a suitable result was found, or they could start-over at any time with a new query. The runner-up and the third-placed systems were "VIRET" [6] and the "vitrivr" system [9], respectively. Both tools supported novel query generation methodologies, such as Query-by-Sketch, which facilitated users to draw a query and Query-by-Example, allowing a user to search for visually similar keyframes to a selected one.…”

Section: Related Systemsmentioning

confidence: 99%

A VR Interface for Browsing Visual Spaces at VBS2021

Tran

Nguyen

et al. 2021

MultiMedia Modeling

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The Video Browser Showdown (VBS) is an annual competition in which each participant prepares an interactive video retrieval system and partakes in a live comparative evaluation at the annual MMM Conference. In this paper, we introduce Eolas, which is a prototype video/image retrieval system incorporating a novel virtual reality (VR) interface. For VBS'21, Eolas represented each keyframe of the collection by an embedded feature in a latent vector space, into which a query would also be projected to facilitate retrieval within a VR environment. A user could then explore the space and perform one of a number of filter operations to traverse the space and locate the correct result.

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VIRET at Video Browser Showdown 2020

Cited by 14 publications

References 19 publications

Is the Reign of Interactive Search Eternal? Findings from the Video Browser Showdown 2020

Is the Reign of Interactive Search Eternal? Findings from the Video Browser Showdown 2020

The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

A VR Interface for Browsing Visual Spaces at VBS2021

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