“Sentetik Büyük Veri” İnşasında Kullanılan Desen Yayma Yaklaşımlarının İncelenmesi

Gaffari Çelik; Muhammed Fatih Talu

Research Article

“Sentetik Büyük Veri” İnşasında Kullanılan Desen Yayma Yaklaşımlarının İncelenmesi

Year 2018, Volume: 3 Issue: 2, 24 - 34, 15.09.2018

Abstract

Derin öğrenme yaklaşımlarının performansı, eğitim aşamasında
kullanılan veri kümesinin büyüklüğü ile doğru orantılıdır. Bu nedenle günümüzde
imge sınıflandırma, bölütleme veya nesne yakalama gibi problemlerin çözümü için
büyük veri kümeleri inşa edilmektedir. Örneğin Alexnet veritabanı 1.2M imge ve
1K kategoriye; Imagenet, 15M imge ve 22K kategoriye; Yahoo Flickr, 100M imge ve
2K kategoriye sahiptir. Bu veri kümeleriyle eğitilen derin ağların doğruluk
oranı oldukça yüksektir. Ancak imgeleri kategorilere atama işleminin manuel
yapılması, hiç şüphesiz derin öğrenme yaklaşımlarının en büyük dezavantajıdır.
İmgeleri kategorize etme (etiketleme), oldukça zahmetli ve hata eğilimi yüksek
bir işlemdir. Bu zorluğu ve hata ihtimalini kaldırılabilmek için gerçek imgeler
yerine, sentetik imgeleri içeren veri kümelerinin kullanımı önerilmektedir.
Sentetik imge üretimi, şekil ve desen üretimi aşamalarını içermektedir. Bir
nesnenin sentetik olarak üretilebilmesi şekil ve desen tanımlayıcı modellerin
inşasıyla mümkündür.

Bu çalışma, desen tanımlayıcı yöntemleri (Parça, Piksel, Piramit)
kapsamaktadır. Bu yöntemler, gerçek bir imgeden alınan küçük bir desen
bilgisinden yola çıkarak deseni yayma ve yüksek boyutlu imge üretmeyi
amaçlamaktadır. Doğruluk, zaman ve gürültü duyarlılığı kıstaslarıyla yapılan
kıyaslama sonucunda parça tabanlı yöntemin en elverişli desen yayma yöntemi
olduğu kanaatine varılmıştır.

Keywords

Desen yayma, piksel tabanlı, parça tabanlı, piramit tabanlı

References

[1] Pu Y, Yuan X, Stevens A, Li C, Carin L. “A Deep Generative Deconvolutional Image Model”. Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Cadiz, Spain, 7-11 May 2016.
[2] Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks. Editors: Fleet D, Pajdla T, Schiele B, Tuytelaars T. Computer Vision – ECCV 2014, 818–833, Springer, Berlin, Germany 2014.
[3] Ergen B, Baykara M. “Texture based feature extraction methods for content based medical image retrieval systems”. Bio-Medical Materials and Engineering, 24(2014):3055-3062, 2014.
[4] Celaya-Padilla J M, Galvan T C E , Delgado C J R , Galvan-Tejada I, Sandoval E I. “Multi-seed texture synthesis to fast image patching”. Procedia Engineering, 35, 210–216, 2012.
[5] Wei L-Y, Lefebvre S, Kwatra V, Turk G. “State of the Art in Example-based Texture Synthesis”. Inria Headquarters and research centres, Rocquencourt , France, State Art Report, 93–117, 2009.
[6] Efros A A, Leung T K., “Texture synthesis by non-parametric sampling,” Proc. Seventh IEEE Int. Conf. on Comput. Vis., Corfu, Greece, 20-27 September 1999.
[7] Shah R. “Texture Synthesis”. http://rajvishah.weebly.com/uploads/6/3/0/9/6309814/texture_synthesis_final_report.pdf .(12.10.2017).
[8] Hisham M B, Yaakob S N, Raof R A A, Nazren A B A, Wafi N M. “Template Matching using Sum of Squared Difference and Normalized Cross Correlation”. 2015 IEEE Student Conference on Research Development (SCOReD), Kuala Lumpur, Malaysia, 13-14 Dec. 2015.
[9] Liang L I N, Liu C E, Xu Y, Guo B. “Real-Time Texture Synthesis by Patch-Based Sampling”. ACM Transactions on Graphics, 20(3), 127–150, 2001.
[10] Malm P, Brun A, Bengtsson E. “Simulation of bright-field microscopy images depicting pap-smear specimen”. Cytometry. Part A, 87(3), 212–226, 2015.
[11] Vinod Kumar R S, Arivazhagan S. “Adaptive Patch Based Texture Synthesis Using Wavelet”. 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies, Thuckafay, India ,21-22 July 2011.
[12] Efros A A, Freeman W T. “Image quilting for texture synthesis and transfer”. Proc. 28th Annu. Conf. Comput. Graph. Interact. Tech. - SIGGRAPH ’01, Los Angeles, CA, USA, 12-17 August 2001.
[13] Heeger D J, Bergen J R. “Pyramid-based texture analysis/synthesis”. Proceedings Book of International Conference on Image Processing, Washington, DC, USA, 23-26 Oct. 1995.
[14] De Ville D V, Guerquin-Kern M, Unser M. “Pyramid-based texture synthesis using local orientation and multidimensional histogram matching”. SPIE Optical Engineering & Applications, San Diego, California, USA, 4 September 2009.
[15] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. “Image quality assessment: From error visibility to structural similarity”. IEEE Transactions on Image Processing, 13(4), 600–612, 2004.

Invesitigation of Pattern Spreading Approaches Used In Construction of "Synthetic Large Data"

Year 2018, Volume: 3 Issue: 2, 24 - 34, 15.09.2018

Gaffari Çelik Muhammed Fatih Talu

Abstract

The performance of deep learning approaches is directly
proportional to the size of the data set used in the training phase. For this
reason, large data sets are currently being built to solve problems such as
image classification, segmentation or object capture. For example Alexnet
database 1.2M image and 1K categorie; Imagenet, 15M image and 22K categorie;
Yahoo Flickr has 100M image and 2K categorization. However, manual assignment
of imagery to categories is undoubtedly the greatest disadvantage of deep
learning approaches. Categorizing images (labeling) is a very troublesome and
error-prone process. In order to remove the possibility of this difficulty and
error, it is suggested to use data sets containing synthetic images instead of
real images. Synthetic image production includes phases of pattern and pattern
production. It is possible to synthetically produce an object by constructing
shape and pattern descriptive models.

This study covers pattern descriptive methods (Patch, Pixel,
Pyramid). These methods are aimed at generating a high-dimensional image by
spreading the pattern out of a small pattern information obtained from a real
image. As a result of comparison with accuracy, time and noise sensitivity
criteria, the pach-based method is considered to be the most suitable pattern
spreading method.

Keywords

Pattern spread, pixel-based, patch-based, pyramid-based

References

[1] Pu Y, Yuan X, Stevens A, Li C, Carin L. “A Deep Generative Deconvolutional Image Model”. Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Cadiz, Spain, 7-11 May 2016.
[2] Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks. Editors: Fleet D, Pajdla T, Schiele B, Tuytelaars T. Computer Vision – ECCV 2014, 818–833, Springer, Berlin, Germany 2014.
[3] Ergen B, Baykara M. “Texture based feature extraction methods for content based medical image retrieval systems”. Bio-Medical Materials and Engineering, 24(2014):3055-3062, 2014.
[4] Celaya-Padilla J M, Galvan T C E , Delgado C J R , Galvan-Tejada I, Sandoval E I. “Multi-seed texture synthesis to fast image patching”. Procedia Engineering, 35, 210–216, 2012.
[5] Wei L-Y, Lefebvre S, Kwatra V, Turk G. “State of the Art in Example-based Texture Synthesis”. Inria Headquarters and research centres, Rocquencourt , France, State Art Report, 93–117, 2009.
[6] Efros A A, Leung T K., “Texture synthesis by non-parametric sampling,” Proc. Seventh IEEE Int. Conf. on Comput. Vis., Corfu, Greece, 20-27 September 1999.
[7] Shah R. “Texture Synthesis”. http://rajvishah.weebly.com/uploads/6/3/0/9/6309814/texture_synthesis_final_report.pdf .(12.10.2017).
[8] Hisham M B, Yaakob S N, Raof R A A, Nazren A B A, Wafi N M. “Template Matching using Sum of Squared Difference and Normalized Cross Correlation”. 2015 IEEE Student Conference on Research Development (SCOReD), Kuala Lumpur, Malaysia, 13-14 Dec. 2015.
[9] Liang L I N, Liu C E, Xu Y, Guo B. “Real-Time Texture Synthesis by Patch-Based Sampling”. ACM Transactions on Graphics, 20(3), 127–150, 2001.
[10] Malm P, Brun A, Bengtsson E. “Simulation of bright-field microscopy images depicting pap-smear specimen”. Cytometry. Part A, 87(3), 212–226, 2015.
[11] Vinod Kumar R S, Arivazhagan S. “Adaptive Patch Based Texture Synthesis Using Wavelet”. 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies, Thuckafay, India ,21-22 July 2011.
[12] Efros A A, Freeman W T. “Image quilting for texture synthesis and transfer”. Proc. 28th Annu. Conf. Comput. Graph. Interact. Tech. - SIGGRAPH ’01, Los Angeles, CA, USA, 12-17 August 2001.
[13] Heeger D J, Bergen J R. “Pyramid-based texture analysis/synthesis”. Proceedings Book of International Conference on Image Processing, Washington, DC, USA, 23-26 Oct. 1995.
[14] De Ville D V, Guerquin-Kern M, Unser M. “Pyramid-based texture synthesis using local orientation and multidimensional histogram matching”. SPIE Optical Engineering & Applications, San Diego, California, USA, 4 September 2009.
[15] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. “Image quality assessment: From error visibility to structural similarity”. IEEE Transactions on Image Processing, 13(4), 600–612, 2004.

There are 15 citations in total.

Details

Primary Language	Turkish
Subjects	Computer Software
Journal Section	PAPERS
Authors	Gaffari Çelik 0000-0001-5658-9529 Muhammed Fatih Talu 0000-0003-1166-8404
Publication Date	September 15, 2018
Submission Date	April 7, 2018
Acceptance Date	September 26, 2018
Published in Issue	Year 2018 Volume: 3 Issue: 2

Cite

APA	Çelik, G., & Talu, M. F. (2018). “Sentetik Büyük Veri” İnşasında Kullanılan Desen Yayma Yaklaşımlarının İncelenmesi. Computer Science, 3(2), 24-34.