Evaluating a Bayesian-like relevance feedback model with text-to-image search initialization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10456792" target="_blank" >RIV/00216208:11320/22:10456792 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Uy8OasMQXW" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Uy8OasMQXW</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-022-14046-w" target="_blank" >10.1007/s11042-022-14046-w</a>

Result language

angličtina

Original language name

Evaluating a Bayesian-like relevance feedback model with text-to-image search initialization

Original language description

Although interactive video retrieval systems often boost search effectiveness, their smart design and optimal usage remains a true challenge. Since verification of design choices or search strategies with real users is tedious and unwieldy task, research efforts in interactive video search area focus also on options for automatic evaluations. This paper contributes to the area with an analysis of artificial user models for relevance feedback based video retrieval systems. Using a state-of-the-art system SOMHunter utilizing the W2VV++ text-image search model, several studies were performed. First, a study without search guidelines was organized with 34 users trying to solve known-item search tasks in a simplified version of SOMHunter. The results of the study were thoroughly analyzed and its data were used to train several artificial user models simulating relevance feedback. The models were evaluated with respect to a second study, where 50 displays of images were annotated by real users. The most promising artificial user model wPCU was selected for simulations analyzing performance of relevance feedback based browsing with different strategies. In a third study, 17 real users achieved on average 70% success rate for a new set of challenging known-item search tasks, strictly following the recommended search strategy. Furthermore, a similar performance for the same set of tasks was predicted by the wPCU model trained with data from the first study. The results and future challenges are thoroughly discussed.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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

2022

Confidentiality

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

Name of the periodical

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

1573-7721

Volume of the periodical

82

Issue of the periodical within the volume

June

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

37

Pages from-to

22305-22341

UT code for WoS article

000878461100002

EID of the result in the Scopus database

2-s2.0-85141208211