Enhanced VIREO KIS at VBS 2018

Nguyen, Phuong Anh; Lu, Yijie; Zhang, Hao; Ngo, Chong‐Wah

doi:10.1007/978-3-319-73600-6_42

Cited by 12 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For color or semantic sketches, vitrivr supports a plethora of features [51,56], VERGE clusters to twelve predefined colors using the Color Layout MPEG-7 descriptor, and HTW uses a handcrafted low-level feature [18]. VIREO [42] and VISIONE [1] also support sketch search, with VISIONE extracting dominant colors with pretrained color hash tables [5,72] and objects using pretrained neural networks [47,48,78]. CollageHunter allows image collages, which enable localization of example image queries on a canvas.…”

Section: Image and Sketch Searchmentioning

confidence: 99%

Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

Heller

Gsteiger

Bailer

et al. 2022

Int J Multimed Info Retr

View full text Add to dashboard Cite

The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.

show abstract

Section: Image and Sketch Searchmentioning

confidence: 99%

Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

Heller

Gsteiger

Bailer

et al. 2022

Int J Multimed Info Retr

View full text Add to dashboard Cite

show abstract

“…The VIREO system provides three search modalities for users [47]. They can formulate a query by using color sketch [45], concepts [46] and free text [33]. The result for color sketch retrieval is based on a simple but effective color sketch method [45].…”

Section: Viret (Charles University Czech Republic)mentioning

confidence: 99%

“…They can formulate a query by using color sketch [45], concepts [46] and free text [33]. The result for color sketch retrieval is based on a simple but effective color sketch method [45]. VIREO also extracts multiple kinds of concepts from videos such as objects, actions, places for users to do concept search.…”

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.

View full text Add to dashboard Cite

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.

show abstract

“…Boolean expression of query terms has been exploited in manual [46] and interactive search [34]. An example of query is Find shots of drinking beverage but not wine or beer.…”

Section: Boolean Queriesmentioning

confidence: 99%

Interpretable Embedding for Ad-Hoc Video Search

Ngo

2020

Proceedings of the 28th ACM International Conference on Multimedia

Self Cite

View full text Add to dashboard Cite

Answering query with semantic concepts has long been the mainstream approach for video search. Until recently, its performance is surpassed by concept-free approach, which embeds queries in a joint space as videos. Nevertheless, the embedded features as well as search results are not interpretable, hindering subsequent steps in video browsing and query reformulation. This paper integrates feature embedding and concept interpretation into a neural network for unified dual-task learning. In this way, an embedding is associated with a list of semantic concepts as an interpretation of video content. This paper empirically demonstrates that, by using either the embedding features or concepts, considerable search improvement is attainable on TRECVid benchmarked datasets. Concepts are not only effective in pruning false positive videos, but also highly complementary to concept-free search, leading to large margin of improvement compared to state-of-the-art approaches. CCS CONCEPTS• Computing methodologies → Neural networks; • Information systems → Video search.

show abstract

Enhanced VIREO KIS at VBS 2018

Cited by 12 publications

References 10 publications

Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

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

Interpretable Embedding for Ad-Hoc Video Search

Contact Info

Product

Resources

About