Fast indexing and searching strategies for feature-based image database systems

Li Wei Kang*, Jin Jang Leou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Because visual data require a large amount of memory and computing power for storage and processing, it is greatly desired to efficiently index and retrieve the visual information from image database systems. We propose efficient indexing and searching strategies for feature-based image database systems, in which uncompressed and compressed domain image features are employed. Each query or stored image is represented by a set of features extracted from the image. The weighted square sum error distance is employed to evaluate the ranks of retrieved images. Many fast clustering and searching techniques exist for the square sum error distance used in vector quantization (VQ), in which different features have identical weighting coefficients. In practice, different features may have different dynamic ranges and different importances, i.e., different features may have different weighting coefficients. We derive a set of inequalities based on the weighted square sum error distance and employ it to speed up the indexing (clustering) and searching procedures for feature-based image database systems. Good simulation results show the feasibility of the proposed approaches.

Original languageEnglish
Article number013019
Pages (from-to)1-14
Number of pages14
JournalJournal of Electronic Imaging
Volume14
Issue number1
DOIs
Publication statusPublished - 2005 Jan
Externally publishedYes

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering

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