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Support vector machines for texture classification

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Support vector machines for texture classification. / Kim, Kwang In; Jung, Keechul; Park, Se Hyun et al.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 11, 2002, p. 1542-1550.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Kim, KI, Jung, K, Park, SH & Kim, HJ 2002, 'Support vector machines for texture classification', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1542-1550. https://doi.org/10.1109/TPAMI.2002.1046177

APA

Kim, K. I., Jung, K., Park, S. H., & Kim, H. J. (2002). Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11), 1542-1550. https://doi.org/10.1109/TPAMI.2002.1046177

Vancouver

Kim KI, Jung K, Park SH, Kim HJ. Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002;24(11):1542-1550. doi: 10.1109/TPAMI.2002.1046177

Author

Kim, Kwang In ; Jung, Keechul ; Park, Se Hyun et al. / Support vector machines for texture classification. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002 ; Vol. 24, No. 11. pp. 1542-1550.

Bibtex

@article{3a63fe8dd5e047fc97a291ebd2b2dfb3,
title = "Support vector machines for texture classification",
abstract = "This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods. One-against-others decomposition is adopted to apply binary SVMs to multitexture classification, plus a neural network is used as an arbitrator to make final classifications from several one-against-others SVM outputs. Experimental results demonstrate the effectiveness of SVMs in texture classification. ",
author = "Kim, {Kwang In} and Keechul Jung and Park, {Se Hyun} and Kim, {Hang Joon}",
year = "2002",
doi = "10.1109/TPAMI.2002.1046177",
language = "English",
volume = "24",
pages = "1542--1550",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
publisher = "IEEE Computer Society",
number = "11",

}

RIS

TY - JOUR

T1 - Support vector machines for texture classification

AU - Kim, Kwang In

AU - Jung, Keechul

AU - Park, Se Hyun

AU - Kim, Hang Joon

PY - 2002

Y1 - 2002

N2 - This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods. One-against-others decomposition is adopted to apply binary SVMs to multitexture classification, plus a neural network is used as an arbitrator to make final classifications from several one-against-others SVM outputs. Experimental results demonstrate the effectiveness of SVMs in texture classification.

AB - This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods. One-against-others decomposition is adopted to apply binary SVMs to multitexture classification, plus a neural network is used as an arbitrator to make final classifications from several one-against-others SVM outputs. Experimental results demonstrate the effectiveness of SVMs in texture classification.

U2 - 10.1109/TPAMI.2002.1046177

DO - 10.1109/TPAMI.2002.1046177

M3 - Journal article

VL - 24

SP - 1542

EP - 1550

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 11

ER -