FashionCapsNet: Kapsül Ağları ile Kıyafet Sınıflandırma

Year 2020, Volume: 13 Issue: 1, 87 - 96, 31.01.2020

Furkan Kınlı , Furkan Kıraç

https://doi.org/10.17671/gazibtd.580222

Cited By: 3

Abstract

Konvolüsyonel Sinir Ağları (KSA) görsel ilişkili derin öğrenme çalışmalarında en sık kullanılan mimarilerden biridir. Popülaritesine rağmen, KSA’lar ortaklama işlemi yüzünden konumsal bilgi kaybı ve afin dönüşümlerine dayanıklı olmama gibi bazı yerleşik sınırlamalara sahiptir. Öte yandan, gruplanmış nöronlardan oluşan Kapsül Ağları, özgün yönlendirme algoritmalarının yardımıyla, nesnenin yüksek boyutlu poz konfigürasyonunu da öğrenme kapasitesine sahiptir. Bu çalışmada dinamik yönlendirme algoritmasını kullanan Kapsül Ağları’nın kıyafet sınıflandırma performansını inceledik. Bu amaçla, arka arkaya yerleştirilmiş 4 Konvolüsyonel katmanlı bir Kapsül Ağ mimarisi (FashionCapsNet) önerdik, ve bu modeli 46 kategoriye ayrılmış 290 bin kıyafet resmi içeren DeepFashion adlı veri seti ile eğittik. Akabinde, modelimizin ve DeepFashion veri seti ile eğitilmiş CNN tabanlı en gelişmiş metotların kategori sınıflandırma sonuçlarını karşılaştırdık. Çalışmamızın sonucunda, FashionCapsNet, kıyafet sınıflandırma için %83,81’lik en yüksek-3 başarım oranı ve %89,83’lük en yüksek-5 başarım oranı sonuçlarını elde etmiştir. Bu rakamlara dayanarak, FashionCapsNet, poz konfigürasyonunu ihmal eden eski metotları açık bir şekilde geride bırakmıştır, ve poz konfigürasyonunu belirgin nokta bilgisinden faydalanarak telafi eden referans çalışmasıyla benzer bir performans göstermiştir. Son olarak, görece yeni olan Kapsül Ağları üzerine yapılacak araştırmalardaki gelişmeler sayesinde, önerdiğimiz bu modelin (FashionCapsNet) kıyafet sınıflandırma performansında ekstra bir artış gözlemlenebilir.

Keywords

derin öğrenme, kapsül ağları, kıyafet sınıflandırma, moda analizi

FashionCapsNet: Clothing Classification with Capsule Networks

Abstract

Convolutional Neural Networks (CNNs) are one of the most commonly used architectures for image-related deep learning studies. Despite its popularity, CNNs have some intrinsic limitations such as losing some of the spatial information and not being robust to affine transformations due to pooling operations. On the other hand, Capsule Networks are composed of groups of neurons, and with the help of its novel routing algorithms, they have the capability for learning high dimensional pose configuration of the objects as well. In this study, we investigate the performance of brand-new Capsule Networks using dynamic routing algorithm on the clothing classification task. To achieve this, we propose 4-layer stacked-convolutional Capsule Network architecture (FashionCapsNet), and train this model on DeepFashion dataset that contains 290k clothing images over 46 different categories. Thereafter, we compare the category classification results of our proposed design and the other state-of-the-art CNN-based methods trained on DeepFashion dataset. As a result of the experimental study, FashionCapsNet achieves 83.81% top-3 accuracy, and 89.83% top-5 accuracy on the clothing classification. Based upon these figures, FashionCapsNet clearly outperforms the earlier methods that neglect pose configuration, and has comparable performance to the baseline study that utilizes an additional landmark information to recover pose configuration. Finally, in the future, proposed FashionCapsNet may inherit extra performance boost on the clothing classification due to advances in the relatively new Capsule Network research.

Keywords

deep learning, capsule networks, clothing classification, fashion analysis

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