Using relevance feedback in content-based image metasearch

Ana B. Benitez; Mandis Beigi; Shih-Fu Chang

doi:10.1109/4236.707692

IEEE Internet Computing

Paper

01 Jul 1998

Using relevance feedback in content-based image metasearch

View publication

Abstract

MetaSeek is an image metasearch engine developed to explore me query of large, distributed, online visual information systems. The current implementation integrates user feedback into a performance-ranking mechanism.

Review

Visually searching the web for content

John R. Smith, Shih-Fu Chang

IEEE Multimedia

Paper

Semi-supervised learning using greedy max-cut

Jun Wang, Tony Jebara, et al.

JMLR

Conference paper

Using self-authentication-and-recovery images for error concealment in wireless environments

Ching-Yung Lin, Daby Sow, et al.

ITCom 2001

Paper

Query-adaptive image search with hash codes

Yu-Gang Jiang, Jun Wang, et al.

IEEE TMM

View all publications

Abstract

Related

Visually searching the web for content

Semi-supervised learning using greedy max-cut

Using self-authentication-and-recovery images for error concealment in wireless environments

Query-adaptive image search with hash codes