Image and video indexing using vector quantization

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. We note that image/video indexing and compression are typically pursued independently. This reduces the storage efficiency and may degrade the system performance. In this paper, we present novel algorithms based on vector quantization (VQ) for indexing of compressed images and video. To start with, the images are compressed using VQ. 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 index to access the image. We also propose an algorithm for indexing compressed video sequences. Here, each frame is encoded in the intraframe mode using VQ. The labels are used for the segmentation of a video sequence into shots, and for indexing the representative frame of each shot. The proposed techniques not only provide fast access to stored visual data, but also combine compression and indexing. The average retrieval rates are 95% and 94% at compression ratios of 16:1 and 64:1, respectively. The corresponding cut detection rates are 97% and 90%, respectively.

Original languageEnglish (US)
Pages (from-to)43-50
Number of pages8
JournalMachine Vision and Applications
Volume10
Issue number2
StatePublished - 1997
Externally publishedYes

Fingerprint

Vector quantization
Labels
Bandwidth
Data storage equipment

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition

Cite this

Image and video indexing using vector quantization. / Idris, F.; Panchanathan, Sethuraman.

In: Machine Vision and Applications, Vol. 10, No. 2, 1997, p. 43-50.

Research output: Contribution to journalArticle

@article{6e6ff61bd610489c962186cb288424d7,
title = "Image and video indexing using vector quantization",
abstract = "Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. We note that image/video indexing and compression are typically pursued independently. This reduces the storage efficiency and may degrade the system performance. In this paper, we present novel algorithms based on vector quantization (VQ) for indexing of compressed images and video. To start with, the images are compressed using VQ. 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 index to access the image. We also propose an algorithm for indexing compressed video sequences. Here, each frame is encoded in the intraframe mode using VQ. The labels are used for the segmentation of a video sequence into shots, and for indexing the representative frame of each shot. The proposed techniques not only provide fast access to stored visual data, but also combine compression and indexing. The average retrieval rates are 95{\%} and 94{\%} at compression ratios of 16:1 and 64:1, respectively. The corresponding cut detection rates are 97{\%} and 90{\%}, respectively.",
author = "F. Idris and Sethuraman Panchanathan",
year = "1997",
language = "English (US)",
volume = "10",
pages = "43--50",
journal = "Machine Vision and Applications",
issn = "0932-8092",
publisher = "Springer Verlag",
number = "2",

}

TY - JOUR

T1 - Image and video indexing using vector quantization

AU - Idris, F.

AU - Panchanathan, Sethuraman

PY - 1997

Y1 - 1997

N2 - Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. We note that image/video indexing and compression are typically pursued independently. This reduces the storage efficiency and may degrade the system performance. In this paper, we present novel algorithms based on vector quantization (VQ) for indexing of compressed images and video. To start with, the images are compressed using VQ. 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 index to access the image. We also propose an algorithm for indexing compressed video sequences. Here, each frame is encoded in the intraframe mode using VQ. The labels are used for the segmentation of a video sequence into shots, and for indexing the representative frame of each shot. The proposed techniques not only provide fast access to stored visual data, but also combine compression and indexing. The average retrieval rates are 95% and 94% at compression ratios of 16:1 and 64:1, respectively. The corresponding cut detection rates are 97% and 90%, respectively.

AB - Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. We note that image/video indexing and compression are typically pursued independently. This reduces the storage efficiency and may degrade the system performance. In this paper, we present novel algorithms based on vector quantization (VQ) for indexing of compressed images and video. To start with, the images are compressed using VQ. 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 index to access the image. We also propose an algorithm for indexing compressed video sequences. Here, each frame is encoded in the intraframe mode using VQ. The labels are used for the segmentation of a video sequence into shots, and for indexing the representative frame of each shot. The proposed techniques not only provide fast access to stored visual data, but also combine compression and indexing. The average retrieval rates are 95% and 94% at compression ratios of 16:1 and 64:1, respectively. The corresponding cut detection rates are 97% and 90%, respectively.

UR - http://www.scopus.com/inward/record.url?scp=0030646511&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030646511&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0030646511

VL - 10

SP - 43

EP - 50

JO - Machine Vision and Applications

JF - Machine Vision and Applications

SN - 0932-8092

IS - 2

ER -