Manifold-ranking-based keyword propagation for image retrieval

Hanghang Tong, Jingrui He, Mingjing Li, Wel Ying Ma, Hong Jiang Zhang, Changshui Zhang

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straightforward manner by exploring the relationship among all images in the feature space in the learning stage. In relevance feedback, the feedback information can be naturally incorporated to refine the retrieval result by additional propagation processes. In order to speed up the convergence of the query concept, we adopt two active learning schemes to select images during relevance feedback. Furthermore, by means of keyword model update, the system can be self-improved constantly. The updating procedure can be performed online during relevance feedback without extra offline training. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images validate the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalEurasip Journal on Applied Signal Processing
Volume2006
DOIs
StatePublished - 2006
Externally publishedYes

Fingerprint

Image retrieval
Feedback
Classifiers
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Signal Processing

Cite this

Manifold-ranking-based keyword propagation for image retrieval. / Tong, Hanghang; He, Jingrui; Li, Mingjing; Ma, Wel Ying; Zhang, Hong Jiang; Zhang, Changshui.

In: Eurasip Journal on Applied Signal Processing, Vol. 2006, 2006, p. 1-10.

Research output: Contribution to journalArticle

Tong, Hanghang ; He, Jingrui ; Li, Mingjing ; Ma, Wel Ying ; Zhang, Hong Jiang ; Zhang, Changshui. / Manifold-ranking-based keyword propagation for image retrieval. In: Eurasip Journal on Applied Signal Processing. 2006 ; Vol. 2006. pp. 1-10.
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