Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/20058
Title: | Object detection and semantic segmentation of fashion images | Authors: | Gievska, Sonja Treneska, Sandra |
Keywords: | object detection, instance segmentation, semantic segmentation, computer vision, fashion images | Issue Date: | 8-May-2020 | Publisher: | Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia | Conference: | CIIT 2020 | 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. | URI: | http://hdl.handle.net/20.500.12188/20058 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
Files in This Item:
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CIIT2020_paper_6.pdf | 1.43 MB | Adobe PDF | View/Open |
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