The correlation between semantic visual similarity and ontology-based concept similarity in effective web image search

Clement H C LEUNG*, Yuanxi Li

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

This paper compares the correlations between visual similarity of real-world images and different ontology-based concept similarity in order to find a novel measurement of the relationship between semantic concepts (objects, scenes) in visual domain besides low level feature extraction. For selected concept pairs, we compute their visual similarity and co-occurrence, which is represented by our Probability-based Visual Distance Model (PVDM). Rather than high computational cost of object recognition, by employing the ontology-based concept similarity into query expansion and filtering, the semantic image search and retrieval precision will be much higher. Furthermore, the latent topic will be mapped into images so that users are possible to retrieval the images with satisfying visual characteristic of the target concept.

Original languageEnglish
Title of host publicationWeb Technologies and Applications - APWeb 2012 International Workshops:SenDe, IDP, IEKB, MBC, Proceedings
Pages125-130
Number of pages6
DOIs
Publication statusPublished - 2012
EventSenDe, IDP, IEKB, MBC, International Workshops, APWeb 2012 - Kunming, China
Duration: 11 Apr 201213 Apr 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7234 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceSenDe, IDP, IEKB, MBC, International Workshops, APWeb 2012
Country/TerritoryChina
CityKunming
Period11/04/1213/04/12

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • image search
  • ontology
  • query expansion
  • visual similarity

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