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

Li Wei Kang*, Jin Jang Leou

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
文章編號013019
頁(從 - 到)1-14
頁數14
期刊Journal of Electronic Imaging
14
發行號1
DOIs
出版狀態已發佈 - 2005 1月
對外發佈

ASJC Scopus subject areas

  • 原子與分子物理與光學
  • 電腦科學應用
  • 電氣與電子工程

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