Fashion Style Recognition Using Component-Dependent Convolutional Neural Networks

The fashion style recognition is important in online marketing applications. Several algorithms have been proposed, but their accuracy is still unsatisfactory. In this paper, we share our proposed method for creating an improved fashion style recognition algorithm, component-dependent convolutional neural networks (CD-CNNs). Given that a lot of fashion styles largely depend on the features of specific body parts or human body postures, first, we obtain images of the body parts and postures by using semantic segmentation and pose estimation algorithms; then, we pre-train CD-CNNs. We perform the classification by the concatenated outputs of CD-CNNs and a support vector machine (SVM). Experimental results using the HipsterWars and FashionStyle14 datasets prove that our method is effective and can improve classification accuracy, namely 85.3% for HipsterWars and 77.7% for FashionStyle14, while those of existing methods were 80.9% for HipsterWars and 72.0% for FashionStyle14.

1. INTRODUCTION

In recent years, several studies that apply computer vision techniques to the fashion industry have been conducted. These include algorithms that search for similar clothes by using images [1], [2] or images and text [3], [4], and algorithms that create images with arbitrary poses from images and posture data [5], [6].