A federated approach for fine-grained classification of fashion apparel

• Main Authors: Tejaswini Mallavarapu, Luke Cranfill, Eun Hye Kim, Reza M. Parizi, John Morris, Junggab Son
• Format: Article
• Language: English
• Published: Elsevier 2021-12-01
• Series: Machine Learning with Applications
• Subjects: Apparel attributes; Apparel classification; Fine-grained classification; Human keypoints detection
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666827021000591

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 such schemes have 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-phase classification of 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.