Image indexing using vector quantization

Fayez M. Idris, Sethuraman Panchanathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

Recently, several image indexing techniques have been reported in the literature. However, these techniques require a large amount of off-line processing, additional storage space and may not be applicable to images stored in compressed form. In this paper, we propose two efficient techniques based on vector quantization (VQ) for image indexing. In VQ, the image to be compressed is decomposed into L-dimensional vectors. Each vector is mapped onto one of a finite set (codebook) of reproduction vectors (codewords). The labels of the codewords are used to represent the image. In the first technique, for each codeword in the codebook, a histogram is generated and stored along with the codeword. We note that the superposition of the histograms of the codewords, which are used to represent an image, is a close approximation of the histogram of the image. This histogram is used as an index to store and retrieve the image. In the second technique, the histogram of the labels of an image is used as an in index to access the image. The proposed techniques provide fast access to the images in the database, have lower storage requirements and combine image compression with image indexing. Simulation results confirm the gains of the proposed techniques in comparison with other techniques reported in the literature.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsWayne Niblack, Ramesh C. Jain
Pages373-380
Number of pages8
StatePublished - Dec 1 1995
Externally publishedYes
EventStorage and Retrieval for Image and Video Databases III - San Jose, CA, USA
Duration: Feb 9 1995Feb 10 1995

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2420
ISSN (Print)0277-786X

Other

OtherStorage and Retrieval for Image and Video Databases III
CitySan Jose, CA, USA
Period2/9/952/10/95

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ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Idris, F. M., & Panchanathan, S. (1995). Image indexing using vector quantization. In W. Niblack, & R. C. Jain (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (pp. 373-380). (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 2420).