Knowledge propagation in large image databases using neighborhood information

Michael E. Houle, Vincent Oria, Shin'ichi Satoh, Jichao Sun

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

6 Scopus citations

Abstract

The aim of this paper is to reduce to a minimum the level of human intervention in the semantic annotation process of images. Ideally, only one copy of each object of interest would be labeled manually, and the labels would then be propagated automatically to all other occurrences of the objects in the database. To that end, we propose a neighbor-based influence propagation approach KProp which builds a voting model and propagates the knowledge associated to some objects to similar objects. We show that KProp can perform efficiently through matrix computations and achieve better performance with fewer labeled examples per object.

Original languageEnglish (US)
Title of host publicationMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
Pages1033-1036
Number of pages4
DOIs
StatePublished - Dec 29 2011
Externally publishedYes
Event19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 - Scottsdale, AZ, United States
Duration: Nov 28 2011Dec 1 2011

Other

Other19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11
CountryUnited States
CityScottsdale, AZ
Period11/28/1112/1/11

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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