Extraction of robust visual phrases using graph mining for image retrieval

Jun Bin Yeh, Chung Hsien Wu

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

8 Citations (Scopus)

Abstract

For the images of an objects with multi-viewpoints, the visual words in a visual phrase may be covered by the object and thus degrades the visual phrase extraction performance. This paper presents an approach to robust visual phrase extraction using graph mining for content-based image retrieval. In this study, the concurrent appearance of two visual words can be estimated over all of the category-related images in a database. The appearance frequencies of the visual words at each image are then used to construct a relation graph of visual words. Graph mining is utilized to mine the frequent dense subgraphs from the visual word relation graphs to extract the visual phrases. Experiments were conducted on the Caltech101 database and the experimental results show that the extracted visual phrases are robust to achieve a better retrieval performance than the pair-wise visual phrase approach.

Original languageEnglish
Title of host publicationISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems
Subtitle of host publicationNano-Bio Circuit Fabrics and Systems
Pages3681-3684
Number of pages4
DOIs
Publication statusPublished - 2010 Aug 31
Event2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010 - Paris, France
Duration: 2010 May 302010 Jun 2

Publication series

NameISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems

Other

Other2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010
CountryFrance
CityParis
Period10-05-3010-06-02

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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