Derin Öǧrenme Modelleri ile Eskiz Siniflandirma

Fevziye Irem Eyiokur; Dogucan Yaman; Hazim Kemal Ekenel

doi:10.1109/SIU.2018.8404417

Derin Öǧrenme Modelleri ile Eskiz Siniflandirma

Translated title of the contribution: Sketch classification with deep learning models

Fevziye Irem Eyiokur, Dogucan Yaman, Hazim Kemal Ekenel

Department of Computer Engineering

Istanbul Technical University

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

2 Citations (Scopus)

Abstract

Sketch classification problem is challenging due to several reasons, such as absence of color and texture information, lack of detailed information of objects, and the quality, which depends on drawing ability of the person. In this study, sketch classification problem is addressed by using deep convolutional neural network models. Specifically, the effect of domain adaptation is examined, when fine-tuning the convolutional neural networks for sketch classification. By employing domain adaptation, the classification accuracy is increased by around 3%. The proposed system, which utilizes VGG-16 network model and performs two-stage fine-tuning, outperforms the previous state-of-the-art approaches on the TU Berlin sketch dataset by reaching 79,72% accuracy.

Translated title of the contribution	Sketch classification with deep learning models
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404417
Publication status	Published - 5 Jul 2018
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

Name	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/SIU.2018.8404417

Cite this

Eyiokur, F. I., Yaman, D., & Ekenel, H. K. (2018). Derin Öǧrenme Modelleri ile Eskiz Siniflandirma. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404417

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title = "Derin {\"O}ǧrenme Modelleri ile Eskiz Siniflandirma",

abstract = "Sketch classification problem is challenging due to several reasons, such as absence of color and texture information, lack of detailed information of objects, and the quality, which depends on drawing ability of the person. In this study, sketch classification problem is addressed by using deep convolutional neural network models. Specifically, the effect of domain adaptation is examined, when fine-tuning the convolutional neural networks for sketch classification. By employing domain adaptation, the classification accuracy is increased by around 3%. The proposed system, which utilizes VGG-16 network model and performs two-stage fine-tuning, outperforms the previous state-of-the-art approaches on the TU Berlin sketch dataset by reaching 79,72% accuracy.",

keywords = "Convolutional neural network, Deep learning, Sketch classification",

author = "Eyiokur, {Fevziye Irem} and Dogucan Yaman and Ekenel, {Hazim Kemal}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

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doi = "10.1109/SIU.2018.8404417",

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

series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

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Eyiokur, FI, Yaman, D & Ekenel, HK 2018, Derin Öǧrenme Modelleri ile Eskiz Siniflandirma. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404417

Derin Öǧrenme Modelleri ile Eskiz Siniflandirma. / Eyiokur, Fevziye Irem; Yaman, Dogucan; Ekenel, Hazim Kemal.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

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

TY - GEN

T1 - Derin Öǧrenme Modelleri ile Eskiz Siniflandirma

AU - Eyiokur, Fevziye Irem

AU - Yaman, Dogucan

AU - Ekenel, Hazim Kemal

PY - 2018/7/5

Y1 - 2018/7/5

N2 - Sketch classification problem is challenging due to several reasons, such as absence of color and texture information, lack of detailed information of objects, and the quality, which depends on drawing ability of the person. In this study, sketch classification problem is addressed by using deep convolutional neural network models. Specifically, the effect of domain adaptation is examined, when fine-tuning the convolutional neural networks for sketch classification. By employing domain adaptation, the classification accuracy is increased by around 3%. The proposed system, which utilizes VGG-16 network model and performs two-stage fine-tuning, outperforms the previous state-of-the-art approaches on the TU Berlin sketch dataset by reaching 79,72% accuracy.

AB - Sketch classification problem is challenging due to several reasons, such as absence of color and texture information, lack of detailed information of objects, and the quality, which depends on drawing ability of the person. In this study, sketch classification problem is addressed by using deep convolutional neural network models. Specifically, the effect of domain adaptation is examined, when fine-tuning the convolutional neural networks for sketch classification. By employing domain adaptation, the classification accuracy is increased by around 3%. The proposed system, which utilizes VGG-16 network model and performs two-stage fine-tuning, outperforms the previous state-of-the-art approaches on the TU Berlin sketch dataset by reaching 79,72% accuracy.

KW - Convolutional neural network

KW - Deep learning

KW - Sketch classification

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DO - 10.1109/SIU.2018.8404417

M3 - Konferans katkısı

AN - SCOPUS:85050795575

T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

SP - 1

EP - 4

BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Y2 - 2 May 2018 through 5 May 2018

ER -

Derin Öǧrenme Modelleri ile Eskiz Siniflandirma

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