Kernel vector approximation files for relevance feedback retrieval in large image databases

Douglas R. Heisterkamp, Jing Peng

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Many data partitioning index methods perform poorly in high dimensional space and do not support relevance feedback retrieval. The vector approximation file (VA-File) approach overcomes some of the difficulties of high dimensional vector spaces, but cannot be applied to relevance feedback retrieval using kernel distances in the data measurement space. This paper introduces a novel KVA-File (kernel VA-File) that extends VA-File to kernel-based retrieval methods. An efficient approach to approximating vectors in an induced feature space is presented with the corresponding upper and lower distance bounds. Thus an effective indexing method is provided for kernel-based relevance feedback image retrieval methods. Experimental results using large image data sets (approximately 100,000 images with 463 dimensions of measurement) validate the efficacy of our method.

Original languageEnglish
Pages (from-to)175-189
Number of pages15
JournalMultimedia Tools and Applications
Volume26
Issue number2
DOIs
StatePublished - 1 Jun 2005

Keywords

  • Content-based image retrieval
  • Indexing
  • Kernel methods
  • Relevance feedback
  • VA-File

Fingerprint Dive into the research topics of 'Kernel vector approximation files for relevance feedback retrieval in large image databases'. Together they form a unique fingerprint.

  • Cite this