Efficient content extraction in compressed images

W. Brent Seales; C. J. Yuan; Michael Brown

doi:10.1109/IVL.1997.629720

Efficient content extraction in compressed images

W. Brent Seales, C. J. Yuan, Michael Brown

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In many contexts, it is desirable for users to be able to extract content directly from image and video data via queries formed dynamically online. Queries over pre-computed features of an image set restrict the user's access to what has been computed by an editor or supervisor of the data. This paper presents an approach to extracting patterns from images and video efficiently in order to support online queries directly on the original image data. Our technique relies on compressed-domain processing, where we perform content extraction on image or video data that are compressed with standard algorithms such as JPEG, MPEG or wavelet schemes.

Original language	English
Title of host publication	Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997
Pages	52-58
Number of pages	7
ISBN (Electronic)	0818679832, 9780818679834
DOIs	https://doi.org/10.1109/IVL.1997.629720
State	Published - 1997
Event	1997 IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997 - San Juan, United States Duration: Jun 20 1997 → Jun 20 1997

Publication series

Name	Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997

Conference

Conference	1997 IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997
Country/Territory	United States
City	San Juan
Period	6/20/97 → 6/20/97

Bibliographical note

Funding Information:
In this paper we have shown that significant speedups are possible in solving the problem of matching patterns in compressed image data. The speedups are available by operating on the data while it is compressed. Without errors from quantization, which is involved in most lossy-coding schemes, the classification results are exact. But even with significant levels of quantization, the eigenspace which serves as the classifier remains robust and performs well. When the eigenspace projection dominates the computation time, as is the case when a small template is matched at all locations in a large image, we achieve a speedup of a factor of 2. In other experiments where a small number of image templates from each compressed image are projected to eigenspace, the decompression time plays a significant role as well and leads to speedups on the order of a factor of five. Acknowledgments We gratefully acknowledge the support of the National Science Foundation for this research under grants IRI-9308415, CDA-9320179 and CDA-9502645.

Funding Information:
We gratefully acknowledge the support of the National Science Foundation for this research under grants IRI-9308415, CDA-9320179 and CDA-9502645.

Publisher Copyright:
© 1997 IEEE.

ASJC Scopus subject areas

Library and Information Sciences
Information Systems
Signal Processing

Access to Document

10.1109/IVL.1997.629720

Cite this

@inproceedings{7e9cbb50dc3e45a6801640cc05521494,

title = "Efficient content extraction in compressed images",

abstract = "In many contexts, it is desirable for users to be able to extract content directly from image and video data via queries formed dynamically online. Queries over pre-computed features of an image set restrict the user's access to what has been computed by an editor or supervisor of the data. This paper presents an approach to extracting patterns from images and video efficiently in order to support online queries directly on the original image data. Our technique relies on compressed-domain processing, where we perform content extraction on image or video data that are compressed with standard algorithms such as JPEG, MPEG or wavelet schemes.",

author = "Seales, {W. Brent} and Yuan, {C. J.} and Michael Brown",

note = "Funding Information: In this paper we have shown that significant speedups are possible in solving the problem of matching patterns in compressed image data. The speedups are available by operating on the data while it is compressed. Without errors from quantization, which is involved in most lossy-coding schemes, the classification results are exact. But even with significant levels of quantization, the eigenspace which serves as the classifier remains robust and performs well. When the eigenspace projection dominates the computation time, as is the case when a small template is matched at all locations in a large image, we achieve a speedup of a factor of 2. In other experiments where a small number of image templates from each compressed image are projected to eigenspace, the decompression time plays a significant role as well and leads to speedups on the order of a factor of five. Acknowledgments We gratefully acknowledge the support of the National Science Foundation for this research under grants IRI-9308415, CDA-9320179 and CDA-9502645. Funding Information: We gratefully acknowledge the support of the National Science Foundation for this research under grants IRI-9308415, CDA-9320179 and CDA-9502645. Publisher Copyright: {\textcopyright} 1997 IEEE.; 1997 IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997 ; Conference date: 20-06-1997 Through 20-06-1997",

year = "1997",

doi = "10.1109/IVL.1997.629720",

language = "English",

series = "Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997",

pages = "52--58",

booktitle = "Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997",

}

Seales, WB, Yuan, CJ & Brown, M 1997, Efficient content extraction in compressed images. in Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997., 5727570, Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997, pp. 52-58, 1997 IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997, San Juan, United States, 6/20/97. https://doi.org/10.1109/IVL.1997.629720

Efficient content extraction in compressed images. / Seales, W. Brent; Yuan, C. J.; Brown, Michael.
Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997. 1997. p. 52-58 5727570 (Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Funding Information: In this paper we have shown that significant speedups are possible in solving the problem of matching patterns in compressed image data. The speedups are available by operating on the data while it is compressed. Without errors from quantization, which is involved in most lossy-coding schemes, the classification results are exact. But even with significant levels of quantization, the eigenspace which serves as the classifier remains robust and performs well. When the eigenspace projection dominates the computation time, as is the case when a small template is matched at all locations in a large image, we achieve a speedup of a factor of 2. In other experiments where a small number of image templates from each compressed image are projected to eigenspace, the decompression time plays a significant role as well and leads to speedups on the order of a factor of five. Acknowledgments We gratefully acknowledge the support of the National Science Foundation for this research under grants IRI-9308415, CDA-9320179 and CDA-9502645. Funding Information: We gratefully acknowledge the support of the National Science Foundation for this research under grants IRI-9308415, CDA-9320179 and CDA-9502645. Publisher Copyright: © 1997 IEEE.

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N2 - In many contexts, it is desirable for users to be able to extract content directly from image and video data via queries formed dynamically online. Queries over pre-computed features of an image set restrict the user's access to what has been computed by an editor or supervisor of the data. This paper presents an approach to extracting patterns from images and video efficiently in order to support online queries directly on the original image data. Our technique relies on compressed-domain processing, where we perform content extraction on image or video data that are compressed with standard algorithms such as JPEG, MPEG or wavelet schemes.

AB - In many contexts, it is desirable for users to be able to extract content directly from image and video data via queries formed dynamically online. Queries over pre-computed features of an image set restrict the user's access to what has been computed by an editor or supervisor of the data. This paper presents an approach to extracting patterns from images and video efficiently in order to support online queries directly on the original image data. Our technique relies on compressed-domain processing, where we perform content extraction on image or video data that are compressed with standard algorithms such as JPEG, MPEG or wavelet schemes.

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M3 - Conference contribution

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BT - Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1997

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ER -

Efficient content extraction in compressed images

Abstract

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Conference

Bibliographical note

ASJC Scopus subject areas

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