Using Deep Learning Techniques Furniture Image Classification

Year 2024, EARLY VIEW, 1 - 1

Kenan Kılıç , Uğur Özcan , Kazım Kılıç , İbrahim Dogru

https://doi.org/10.2339/politeknik.1315328

Abstract

Bu makale, mobilya görüntülerinin sınıflandırılması konusunda yapay zeka tekniklerinin kullanılmasını ele almaktadır. Mobilya sektöründe birçok farklı tasarım ve tarz arasından seçim yapmanın zorluğu, tüketiciler ve satıcılar için bir sorun oluşturmaktadır. Makine öğrenimi algoritmaları ve sinir ağları, mobilya görüntülerini otomatik olarak sınıflandırma sürecinde kullanılmaktadır. Makalenin amacı, mobilya görüntülerinin sınıflandırılmasıyla tüketicilerin ve mobilya endüstrisi profesyonellerinin karşılaştığı sorunları çözmektir. Makalede, mobilya görüntülerinin sınıflandırılması için beş farklı evrişimli sinir ağı mimarisi kullanılmıştır: Alexnet, VGGNet-19, DenseNet-201, Squeezenet1.1 ve ResNet-152. Bu mimarilerin kullanımıyla %98.87 sınıflandırma başarısı elde edilmiştir. Beş farklı mobilya kategorisi (yatak, sandalye, kanepe, döner koltuk ve masa) sınıflandırılmış ve ResNet-152 mimarisiyle %99.96 ROC (Receiver Operating Characteristic) değeri elde edilmiştir. Ayrıca, transfer öğrenme yaklaşımının kullanılmasıyla daha hızlı ve doğru sonuçlar elde edildiği belirtilmiştir. VGG-19 ve SqueezeNet1.1 mimarileri %97.07 ortalama sınıflandırma doğruluğu sağlarken, en düşük doğruluğu Alexnet modeli (%94.15) gerçekleştirmiştir. Derin öğrenme algoritmalarının kullanılmasıyla görüntülerin özellikleri çıkarılmakta ve sınıflandırılmaktadır. Bu çalışma, teknolojinin daha akıllı ve kullanıcı odaklı bir alışveriş deneyimi sunma potansiyeline sahip olduğunu göstermektedir. Aynı zamanda, mobilya üretim ve satışında verimliliği artırarak rekabet avantajı sağlayabilecek bir mobilya sınıflandırma yöntemi sunmaktadır. Çalışmada elde edilen sonuçlar, mobilya görüntülerinin analizi ve sınıflandırılmasında CNN mimarilerinin etkili olduğu göstermiştir.

Keywords

Image Classification, deep learning, computer vision, furniture image, transfer learning.

Derin Öğrenme Tekniklerini Kullanarak Mobilya Görüntüsü Sınıflandırması

Abstract

This paper discusses the use of artificial intelligence (AI) techniques for the classification of furniture images. In the furniture industry, the difficulty of choosing from many different designs and styles poses a problem for consumers and sellers. Machine learning (ML) algorithms and neural networks are used in the process of automatically classifying furniture images. The aim of the paper is to solve the problems faced by consumers and furniture industry professionals with the classification of furniture images. In the paper, five different convolutional neural network architectures are used for furniture image classification: Alexnet, VGGNet-19, DenseNet-201, Squeezenet1.1 and ResNet-152. Using these architectures, 98.87% classification success is achieved. Five different categories of furniture (bed, chair, sofa, swivel chair and table) are classified and an ROC (Receiver Operating Characteristic) value of 99.96% is obtained with the ResNet-152 architecture. In addition, it is reported that faster and more accurate results are obtained by using a transfer learning approach. The VGGNet-19 and SqueezeNet1.1 architectures provided an average classification accuracy of 97.07%, while the Alexnet model (94.15%) achieved the lowest accuracy. Using deep learning algorithms, the features of images are extracted and classified. This study shows that the technology has the potential to deliver a smarter and user-centered shopping experience. It also provides a furniture classification method that can provide a competitive advantage by increasing efficiency in furniture production and sales. The results of the study show that CNN architectures are effective in analyzing and classifying furniture images.

Keywords

Görüntü sınıflandırma, derin öğrenme, bilgisayar görüşü, mobilya görüntüsü, transfer öğrenme

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