LBP Özelliklerine Dayanan Lokasyon Koruyan Projeksiyon (LPP) Boyut Azaltma Metodunun Farklı Sınıflandırıcılar Üzerindeki Performanslarının Karşılaştırılması

Year 2018, , 1101 - 1108, 01.08.2018

Sevcan Aytaç Korkmaz

https://doi.org/10.16984/saufenbilder.349567

Cited By: 1

Abstract

Görüntü hücreleri
Işık Mikroskop yardımıyla alınmıştır. Yerel ikili örüntü (LBP) özellikleri
orijinal görüntüler için elde edilmiştir. Bu görüntülerin LBP özelliklerinin yüksek
boyutu, Lokasyon Koruyan Projeksiyon (LPP) ile daha düşük boyuta indirgenir. Bu
düşük boyutlu veriler Rastgele Orman (RF), Naive Bayes (NB) ve Yapay Sinir
Ağları (ANN) sınıflandırıcısı tarafından sınıflandırılmıştır. Yapılan
sınıflandırma sonuçları daha önceden yapılan çalışmalar ile
karşılaştırılmıştır. ANN sınıflandırıcısıyla elde edilen performans RF ve NB
sınıflandırıcına göre daha yüksektir. Üstelik, ANN sınıflandırıcısında kullanılan
özellik vektör boyutu, RF ve NB sınıflandırıcılarında kullanılan özellik
vektörü boyutundan daha düşüktür. LPP Yöntemine göre ANN, RF ve NB
sınıflandırıcıları ile elde edilen başarı oranları sırasıyla% 96.29,% 74.44 ve%
70.00'dır.

Keywords

yerel ikili örüntü, rastgele orman, lpp, yapay sinir ağları, naive bayes

Comparison of Performance on the Different Classifiers of the Locating Protected Projection (LPP) Dimension Reduction Method Based LBP Features

Abstract

Image cells have
taken with Light Microscope help. The local binary pattern (LBP) features have
obtained for original images. High-dimensional of these LBP features is reduced
to lower-dimensional with Locality Preserving Projections (LPP). These low
dimensional data are classified by the Random Forest (RF), Naive Bayes (NB),
and Artificial Neural Networks (ANN) classifiers. The classification results
are compared with previous studies. The performance achieved with the ANN
classifier is higher than the RF and NB classifiers. Moreover, feature vector
size used in ANN classifier is a lower than feature vector size used in RF and
NB classifiers. The success rates achieved with the ANN, RF, and NB classifiers
is respectively 96.29%, 74.44%,and 70.00% according to LPP Method.

Keywords

local binary pattern features, random forest, lpp, Artificial Neural Networks, Naive Bayes

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