A Framework for Effective Known-item Search in Video

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10401600" target="_blank" >RIV/00216208:11320/19:10401600 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3343031.3351046" target="_blank" >https://doi.org/10.1145/3343031.3351046</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3343031.3351046" target="_blank" >10.1145/3343031.3351046</a>

Result language

angličtina

Original language name

A Framework for Effective Known-item Search in Video

Original language description

Searching for one particular scene in a large video collection (known-item search) represents a challenging task for video retrieval systems. According to the recent results reached at evaluation campaigns, even respected approaches based on machine learning do not help to solve the task easily in many cases. Hence, in addition to effective automatic multimedia annotation and embedding, interactive search is recommended as well. This paper presents a comprehensive description of an interactive video retrieval framework VIRET that successfully participated at several recent evaluation campaigns. Utilized video analysis, feature extraction and retrieval models are detailed as well as several experiments evaluating effectiveness of selected system components. The results of the prototype at the Video Browser Showdown 2019 are highlighted in connection with an analysis of collected query logs. We conclude that the framework comprise a set of effective and efficient models for most of the evaluated known-item search tasks in 1000 hours of video and could serve as a baseline reference approach. The analysis also reveals that the result presentation interface needs improvements for better performance of future VIRET prototypes.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexible models for known-item search in large video collections</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 27th ACM International Conference on Multimedia

ISBN

978-1-4503-6889-6

ISSN

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e-ISSN

—

Number of pages

9

Pages from-to

1777-1785

Publisher name

ACM

Place of publication

New York, NY, USA

Event location

Nice, France

Event date

Oct 21, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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