An unsupervised learning approach to content-based image retrieval

Yixin Chen, James Wang, Robert Krovetz

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

39 Scopus citations

Abstract

"Semantic gap" is an open challenging problem in content-based image retrieval. It rejects the discrepancy between low-level imagery features used by the retrieval algorithm and high-level concepts required by system users. This paper introduces a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), to tackle the semantic gap problem. CLUE is built on a hypothesis that images of the same semantics tend to be clustered. It attempts to narrow the semantic gap by retrieving image clusters based on not only the feature similarity of images to the query, but also how images are similar to each other. CLUE has been tested using examples from a database of about 60,000 general-purpose images. Empirical results demonstrate the effectiveness of CLUE.

Original languageEnglish (US)
Title of host publicationProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
PublisherIEEE Computer Society
Pages197-200
Number of pages4
ISBN (Print)0780379462, 9780780379466
DOIs
StatePublished - Jan 1 2003
Event7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 - Paris, France
Duration: Jul 1 2003Jul 4 2003

Publication series

NameProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
Volume1

Other

Other7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
CountryFrance
CityParis
Period7/1/037/4/03

All Science Journal Classification (ASJC) codes

  • Signal Processing

Fingerprint Dive into the research topics of 'An unsupervised learning approach to content-based image retrieval'. Together they form a unique fingerprint.

Cite this