Known-Item Search in Video: An Eye Tracking-Based Study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10490637" target="_blank" >RIV/00216208:11320/24:10490637 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3652583.3658119" target="_blank" >https://doi.org/10.1145/3652583.3658119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3652583.3658119" target="_blank" >10.1145/3652583.3658119</a>

Result language

angličtina

Original language name

Known-Item Search in Video: An Eye Tracking-Based Study

Original language description

Deep learning has revolutionized multimedia retrieval, yet effectively searching within large video collections remains a complex challenge. This paper focuses on the design and evaluation of known-item search systems, leveraging the strengths of CLIP-based deep neural networks for ranking. At events like the Video Browser Showdown, these models have shown promise in effectively ranking the video frames. While ranking models can be pre-selected automatically based on a benchmark collection, the selection of an optimal browsing interface, crucial for refining top-ranked items, is complex and heavily influenced by user behavior. Our study addresses this by presenting an eye tracking-based analysis of user interaction with different image grid layouts. This approach offers novel insights into search patterns and user preferences, particularly examining the trade-off between displaying fewer but larger images versus more but smaller images. Our findings reveal a preference for grids with fewer images and detail how image similarity and grid position affect user search behavior. These results not only enhance our understanding of effective video retrieval interface design but also set the stage for future advancements in the field.

Czech name

—

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/GA22-21696S" target="_blank" >GA22-21696S: Deep Visual Representations of Unstructured Data</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Article name in the collection

PROCEEDINGS OF THE 14TH ANNUAL ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ICMR 2024

ISBN

979-8-4007-0619-6

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

311-319

Publisher name

ASSOC COMPUTING MACHINERY

Place of publication

NEW YORK

Event location

Phuket

Event date

Jun 10, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001282078400035