A probability model for image annotation

Yong Ge, Richang Hong, Zhiwei Gu, Rong Zhang, Xiuqing Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Automatic image annotation is a promising solution to enable more effective image retrieval by keywords. Traditionally, statistical models for image auto-annotation predicate each annotated keyword independently without considering the correlation of words. In this paper, we propose a novel probability model, in which the correspondence between keywords and image visual tokens/regions and the word-to-word correlation are well combined. We employ the conditional probability to express two kinds of correlation uniformly and obtain the correspondence between keyword and visual feature with the cross-media relevance model (CMRM). Experiments conducted on standard Corel dataset demonstrate the effectiveness of the proposed method for image automatic annotation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
PublisherIEEE Computer Society
Pages827-830
Number of pages4
ISBN (Print)1424410177, 9781424410170
DOIs
StatePublished - 2007
Externally publishedYes
EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
Duration: Jul 2 2007Jul 5 2007

Publication series

NameProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Conference

ConferenceIEEE International Conference onMultimedia and Expo, ICME 2007
Country/TerritoryChina
CityBeijing
Period7/2/077/5/07

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software

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