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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexibilní modely pro hledání známé scény v rozsáhlých kolekcích videa</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

1573-7721

Svazek periodika

82

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

37

Strana od-do

22305-22341

Kód UT WoS článku

000878461100002

EID výsledku v databázi Scopus

2-s2.0-85141208211