Classification in High Resolution Images with Multiple Classifiers

A Monadjemi; BT Thomas; M Mirmehdi

Classification in High Resolution Images with Multiple Classifiers

A Monadjemi
, BT Thomas
, M Mirmehdi

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

We examine the use of high frequency features in high resolution images and demonstrate how they can increase texture classification accuracy when used in combination with lower frequency features. We used eight features, four low frequency and four high frequency, derived from patches of 4032x2688 images. Furthermore, we experiment with both single and multiple classifiers to illustrate the effectiveness of such a combination. Outcomes of classification tests on outdoor scene patches are presented and discussed.

Translated title of the contribution	Classification in High Resolution Images with Multiple Classifiers
Original language	English
Title of host publication	Unknown
Editors	J. J. Villanueva
Publisher	IASTED
Pages	417 - 421
Number of pages	4
ISBN (Print)	0889863543
Publication status	Published - Sept 2002

Bibliographical note

Conference Proceedings/Title of Journal: IASTED Visualization, Imageing and Image Processing VIIP 2002 Conference

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@inproceedings{21cf8be307a94d6dbf49f97f74eaf2de,

title = "Classification in High Resolution Images with Multiple Classifiers",

abstract = "We examine the use of high frequency features in high resolution images and demonstrate how they can increase texture classification accuracy when used in combination with lower frequency features. We used eight features, four low frequency and four high frequency, derived from patches of 4032x2688 images. Furthermore, we experiment with both single and multiple classifiers to illustrate the effectiveness of such a combination. Outcomes of classification tests on outdoor scene patches are presented and discussed. ",

author = "A Monadjemi and BT Thomas and M Mirmehdi",

note = "Conference Proceedings/Title of Journal: IASTED Visualization, Imageing and Image Processing VIIP 2002 Conference",

year = "2002",

month = sep,

language = "English",

isbn = "0889863543",

pages = "417 -- 421",

editor = "Villanueva, \{J. J.\}",

booktitle = "Unknown",

publisher = "IASTED",

}

TY - GEN

T1 - Classification in High Resolution Images with Multiple Classifiers

AU - Monadjemi, A

AU - Thomas, BT

AU - Mirmehdi, M

N1 - Conference Proceedings/Title of Journal: IASTED Visualization, Imageing and Image Processing VIIP 2002 Conference

PY - 2002/9

Y1 - 2002/9

N2 - We examine the use of high frequency features in high resolution images and demonstrate how they can increase texture classification accuracy when used in combination with lower frequency features. We used eight features, four low frequency and four high frequency, derived from patches of 4032x2688 images. Furthermore, we experiment with both single and multiple classifiers to illustrate the effectiveness of such a combination. Outcomes of classification tests on outdoor scene patches are presented and discussed.

AB - We examine the use of high frequency features in high resolution images and demonstrate how they can increase texture classification accuracy when used in combination with lower frequency features. We used eight features, four low frequency and four high frequency, derived from patches of 4032x2688 images. Furthermore, we experiment with both single and multiple classifiers to illustrate the effectiveness of such a combination. Outcomes of classification tests on outdoor scene patches are presented and discussed.

M3 - Conference Contribution (Conference Proceeding)

SN - 0889863543

SP - 417

EP - 421

BT - Unknown

A2 - Villanueva, J. J.

PB - IASTED

ER -

Classification in High Resolution Images with Multiple Classifiers

Abstract

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