A Federated Approach for Fine-Grained Classification of Fashion Apparel

TL;DR

This paper presents a federated approach for detailed classification of fashion apparel attributes from images, enabling in-depth analysis beyond basic categories, with high accuracy and improved over existing CNN methods.

Contribution

It introduces a multi-stage scheme combining segmentation, key point detection, and neural networks for fine-grained fashion attribute classification within the same category.

Findings

Average precision above 93% for all categories

Outperforms existing CNN-based schemes

Effective in classifying detailed fashion attributes

Abstract

As online retail services proliferate and are pervasive in modern lives, applications for classifying fashion apparel features from image data are becoming more indispensable. Online retailers, from leading companies to start-ups, can leverage such applications in order to increase profit margin and enhance the consumer experience. Many notable schemes have been proposed to classify fashion items, however, the majority of which focused upon classifying basic-level categories, such as T-shirts, pants, skirts, shoes, bags, and so forth. In contrast to most prior efforts, this paper aims to enable an in-depth classification of fashion item attributes within the same category. Beginning with a single dress, we seek to classify the type of dress hem, the hem length, and the sleeve length. The proposed scheme is comprised of three major stages: (a) localization of a target item from an input image using semantic segmentation, (b) detection of human key points (e.g., point of shoulder) using a pre-trained CNN and a bounding box, and (c) three phases to classify the attributes using a combination of algorithmic approaches and deep neural networks. The experimental results demonstrate that the proposed scheme is highly effective, with all categories having average precision of above 93.02%, and outperforms existing Convolutional Neural Networks (CNNs)-based schemes.