Biased discriminant analysis with feature line embedding for relevance feedback-based image retrieval

Yu Chen Wang, Chin Chuan Han, Chen Ta Hsieh, Ying Nong Chen, Kuo Chin Fan

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method.

Original languageEnglish
Article number7300420
Pages (from-to)2245-2258
Number of pages14
JournalIEEE Transactions on Multimedia
Volume17
Issue number12
DOIs
StatePublished - Dec 2015

Keywords

  • Biased discriminant analysis
  • content-based image retrieval (CBIR)
  • feature line embedding (FLE)
  • high-level semantic concept
  • low-level image features
  • relevance feedback

Fingerprint

Dive into the research topics of 'Biased discriminant analysis with feature line embedding for relevance feedback-based image retrieval'. Together they form a unique fingerprint.

Cite this