İkili Görüntülerin Sınıflandırılması için Yeni Bir Evrişimsel Sinir Ağı Mimarisi

Abstract

Literatürdeki pek çok çalışma, derin öğrenme algoritmalarını kullanarak renkli görüntülerin sınıflandırılmasına odaklanırken, ikili görüntülerin sınıflandırılmasına yönelik araştırmaların sınırlı olduğu gözlemlenmektedir. İkili görüntüler için tasarlanan Evrişimli Sinir Ağ mimarileri, renkli görüntülere kıyasla genellikle daha düşük performans sergilemekle birlikte, ikili görüntülerdeki giriş verilerinin 8 bitlik renkli görüntülere göre 24 kat daha az bilgi içeriyor olması, işlem hızlarının önemli ölçüde artmasına neden olmaktadır. Bu çalışmanın amacı, yalnızca ikili görüntüler gerektiren uygulamalar —örneğin, imza tanıma, barkod okuma, QR kod tarama ve el yazısı analizi— için yüksek verimlilikle çalışan ağ mimarileri geliştirmektir. Bu hedef doğrultusunda, mevcut katmanlardan yararlanarak yeni bir Bi-CNN ağ mimarisi tasarlanmıştır. Ardından, bu mimarinin performansını artırmaya yönelik özel bir kayıp fonksiyonu geliştirilmiş ve Si-CL(İmza-Sınıflandırma) adı verilen sınıflandırma katmanı, Bi-CNN(İkili CNN)’e entegre edilerek Bi-CL-CNN olarak adlandırılan yeni bir mimari ortaya çıkmıştır. Hem Bi-CNN hem de Bi-CL-CNN, iki farklı veri kümesi üzerinde eğitilmiştir. İlk veri kümesi olan Shape-DU, bu ağları test etmek amacıyla özel olarak oluşturulmuştur. İkinci veri kümesi olan MPEG-7 ise kıyaslama amacıyla kullanılmıştır. Eğitilen ağların performansı, GoogleNet, ResNet50 ve DenseNet201 gibi daha önce eğitilmiş üç ağ ile karşılaştırılmıştır. Deneysel sonuçlar, Bi-CL-CNN ağının, doğruluk ve hesaplama hızı açısından diğer modellerden anlamlı derecede daha iyi performans gösterdiğini ortaya koymuştur. Bu bulgular, ikili görüntü veri kümelerinin işlenmesinde önerilen modellerin sağlamlık ve verimliliğini vurgulamaktadır.

Keywords

• Evrişimli sinir ağı
• derin sinir ağı
• görüntü sınıflandırma
• ikili görüntü
• kayıp fonksiyonu

A Novel Convolutional Neural Network Architecture for the Classification of Binary Images

Abstract

While numerous studies in the literature focus on the classification of color images using deep learning algorithms, there is a notable gap in research dedicated to the classification of binary images. Although Convolutional Neural Networks designed for binary images tend to exhibit lower performance compared to those for color images, their processing speed is significantly faster, as the input data for binary images is reduced by a factor of 24 compared with the 8-bit color images. This study aims to develop network architectures that operate with high efficiency in applications requiring only binary images, such as signature recognition, barcode reading, QR code scanning, and handwriting analysis. For this purpose, a new Bi-CNN(Binary image-CNN) network architecture was designed using existing layers. Then, a special loss function was used to improve the performance of this architecture. By integrating the classification layer called Si-CL(Signature-Classification) into Bi-CNN, a new architecture called Bi-CL-CNN emerged. Both Bi-CNN and Bi-CL-CNN were trained on two datasets. The first dataset, Shape-DU, was specifically created for testing these networks. The second dataset, MPEG-7, serves as a benchmark dataset. The performance of the trained networks is compared with three previously trained networks, namely GoogleNet, ResNet50 and DenseNet201. The empirical evaluation demonstrated that the Bi-CL-CNN network significantly outperformed the other models in both accuracy and computational speed. These findings underscore the robustness and efficiency of the proposed models in handling binary image datasets.

Keywords

• Convolutional neural network
• deep neural network
• image classification
• binary image
• loss funtion

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