Object detection and semantic segmentation of fashion images

Sandra Treneska; Sonja Gievska

Object detection and semantic segmentation of fashion images

Date Issued

2020-05-08

Author(s)

Sandra Treneska

Sonja Gievska

Abstract

Over the past few years, fashion brands have been rapidly implementing computer vision into the fashion industry. Our research objective was to analyse a number of methods suitable for object detection and segmentation of apparel in fashion images. Two types of models are proposed. The first, simpler, is a convolutional neural network used for object detection of clothing items on the Fashion-MNIST dataset and the second, more complex Mask R-CNN model is used for object detection and instance segmentation on the iMaterialist dataset. The performance of the first proposed model reached 93% accuracy. Furthermore, the results from the Mask R-CNN model are visualized.

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CIIT2020_paper_6.pdf

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1.39 MB

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Adobe PDF

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