ConceptRank for search-based image annotation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00100721" target="_blank" >RIV/00216224:14330/18:00100721 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11042-017-4777-8" target="_blank" >http://dx.doi.org/10.1007/s11042-017-4777-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-017-4777-8" target="_blank" >10.1007/s11042-017-4777-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ConceptRank for search-based image annotation

Popis výsledku v původním jazyce

Multimedia information is becoming an ubiquitous part of our lives, which brings an equally ubiquitous need for efficient multimedia retrieval. One of the possible solutions to this problem is to attach text descriptions to multimedia data objects, thus allowing users to utilize traditional text search mechanisms. Search-based annotation techniques attempt to determine the descriptive keywords by analyzing the descriptions of similar, already annotated multimedia objects, which are detected by content-based retrieval techniques. One of the main challenges of this approach is the extraction of semantically connected keywords from the possibly noisy descriptions of similar objects. In this paper, we address this challenge by proposing the ConceptRank, a new keyword ranking algorithm that exploits semantic relationships between candidate keywords and utilizes the random walk mechanism to compute the probability of individual candidates. The effectiveness of the ConceptRank algorithm is evaluated in context of web image annotation. We present a complex image annotation system that includes the ConceptRank component, and compare it to other state-of-the–art annotation techniques.

Název v anglickém jazyce

ConceptRank for search-based image annotation

Popis výsledku anglicky

Multimedia information is becoming an ubiquitous part of our lives, which brings an equally ubiquitous need for efficient multimedia retrieval. One of the possible solutions to this problem is to attach text descriptions to multimedia data objects, thus allowing users to utilize traditional text search mechanisms. Search-based annotation techniques attempt to determine the descriptive keywords by analyzing the descriptions of similar, already annotated multimedia objects, which are detected by content-based retrieval techniques. One of the main challenges of this approach is the extraction of semantically connected keywords from the possibly noisy descriptions of similar objects. In this paper, we address this challenge by proposing the ConceptRank, a new keyword ranking algorithm that exploits semantic relationships between candidate keywords and utilizes the random walk mechanism to compute the probability of individual candidates. The effectiveness of the ConceptRank algorithm is evaluated in context of web image annotation. We present a complex image annotation system that includes the ConceptRank component, and compare it to other state-of-the–art annotation techniques.

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/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

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

Rok uplatnění

2018

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

—

Svazek periodika

77

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

36

Strana od-do

8847-8882

Kód UT WoS článku

000429355800048

EID výsledku v databázi Scopus

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