Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi

Nihan Kazak; Mehmet Koc; Burak Benligiray; Cihan Topal

doi:10.1109/SIU.2016.7495862

Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi

Translated title of the contribution: A comparison of classification methods for local binary patterns

Nihan Kazak, Mehmet Koc, Burak Benligiray, Cihan Topal

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

3 Citations (Scopus)

Abstract

Texture recognition is an important tool used for content-based image retrieval, face recognition, and satellite image classification applications. One of the most successful features for texture recognition is local binary patterns (LBP), which computes local intensity differences for a pixel with respect to its neighbor pixels. In many studies in the literature, histogram based similarity measures are employed to classify LBP features. In this study, we investigate the performance of support vector machines, linear discriminant analysis, and linear regression classifier to improve the success of LBP features. We achieved 84.4% classification success using linear regression classification.

Translated title of the contribution	A comparison of classification methods for local binary patterns
Original language	Turkish
Title of host publication	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	805-808
Number of pages	4
ISBN (Electronic)	9781509016792
DOIs	https://doi.org/10.1109/SIU.2016.7495862
Publication status	Published - 20 Jun 2016
Externally published	Yes
Event	24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Duration: 16 May 2016 → 19 May 2016

Publication series

Name	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

Conference

Conference	24th Signal Processing and Communication Application Conference, SIU 2016
Country/Territory	Turkey
City	Zonguldak
Period	16/05/16 → 19/05/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/SIU.2016.7495862

Cite this

Kazak, N., Koc, M., Benligiray, B., & Topal, C. (2016). Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi. In 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings (pp. 805-808). Article 7495862 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2016.7495862

Kazak, Nihan ; Koc, Mehmet ; Benligiray, Burak et al. / Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi. 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 805-808 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

@inproceedings{e668fab4552d462487657ae12f1364fa,

title = "Yerel Ikili {\"O}r{\"u}nt{\"u}ler I{\c c}in Siniflandirma Y{\"o}ntemlerinin Kar{\c s}ila{\c s}tirilmasi",

abstract = "Texture recognition is an important tool used for content-based image retrieval, face recognition, and satellite image classification applications. One of the most successful features for texture recognition is local binary patterns (LBP), which computes local intensity differences for a pixel with respect to its neighbor pixels. In many studies in the literature, histogram based similarity measures are employed to classify LBP features. In this study, we investigate the performance of support vector machines, linear discriminant analysis, and linear regression classifier to improve the success of LBP features. We achieved 84.4% classification success using linear regression classification.",

keywords = "classification methods, local binary patterns, texture classification, UIUC texture database",

author = "Nihan Kazak and Mehmet Koc and Burak Benligiray and Cihan Topal",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 24th Signal Processing and Communication Application Conference, SIU 2016 ; Conference date: 16-05-2016 Through 19-05-2016",

year = "2016",

month = jun,

day = "20",

doi = "10.1109/SIU.2016.7495862",

language = "T{\"u}rk{\c c}e",

series = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "805--808",

booktitle = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",

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}

Kazak, N, Koc, M, Benligiray, B & Topal, C 2016, Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi. in 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings., 7495862, 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 805-808, 24th Signal Processing and Communication Application Conference, SIU 2016, Zonguldak, Turkey, 16/05/16. https://doi.org/10.1109/SIU.2016.7495862

Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi. / Kazak, Nihan; Koc, Mehmet; Benligiray, Burak et al.
2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 805-808 7495862 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

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

TY - GEN

T1 - Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi

AU - Kazak, Nihan

AU - Koc, Mehmet

AU - Benligiray, Burak

AU - Topal, Cihan

PY - 2016/6/20

Y1 - 2016/6/20

N2 - Texture recognition is an important tool used for content-based image retrieval, face recognition, and satellite image classification applications. One of the most successful features for texture recognition is local binary patterns (LBP), which computes local intensity differences for a pixel with respect to its neighbor pixels. In many studies in the literature, histogram based similarity measures are employed to classify LBP features. In this study, we investigate the performance of support vector machines, linear discriminant analysis, and linear regression classifier to improve the success of LBP features. We achieved 84.4% classification success using linear regression classification.

AB - Texture recognition is an important tool used for content-based image retrieval, face recognition, and satellite image classification applications. One of the most successful features for texture recognition is local binary patterns (LBP), which computes local intensity differences for a pixel with respect to its neighbor pixels. In many studies in the literature, histogram based similarity measures are employed to classify LBP features. In this study, we investigate the performance of support vector machines, linear discriminant analysis, and linear regression classifier to improve the success of LBP features. We achieved 84.4% classification success using linear regression classification.

KW - classification methods

KW - local binary patterns

KW - texture classification

KW - UIUC texture database

UR - http://www.scopus.com/inward/record.url?scp=84982824307&partnerID=8YFLogxK

U2 - 10.1109/SIU.2016.7495862

DO - 10.1109/SIU.2016.7495862

M3 - Konferans katkısı

AN - SCOPUS:84982824307

T3 - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

SP - 805

EP - 808

BT - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th Signal Processing and Communication Application Conference, SIU 2016

Y2 - 16 May 2016 through 19 May 2016

ER -

Kazak N, Koc M, Benligiray B, Topal C. Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi. In 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 805-808. 7495862. (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). doi: 10.1109/SIU.2016.7495862

Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi

Abstract

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