Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Stefan Rohrmanstorfer, Mikhail Komarov and Felix Mödritscher
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Stefan Rohrmanstorfer: Department Computer Science, University of Applied Science Technikum Wien, 1200 Vienna, Austria
Mikhail Komarov: Department of Business Informatics, Graduate School of Business, National Research University Higher School of Economics, 101000 Moscow, Russia
Felix Mödritscher: Department Computer Science, University of Applied Science Technikum Wien, 1200 Vienna, Austria

Mathematics, 2021, vol. 9, issue 6, 1-32

Abstract: With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

Keywords: image classification; neural network; convolutional; machine learning; fashion; apparel (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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