| Author: | Wong, Chai Kwok |
| Title: | Classifying fashion styles using deep learning |
| Advisors: | Chan, Keith (COMP) |
| Degree: | M.Sc. |
| Year: | 2018 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Machine learning Image processing -- Digital techniques |
| Department: | Department of Computing |
| Pages: | v, 78 pages : color illustrations |
| Language: | English |
| Abstract: | Deep learning, or deep neural network, is a subclass of machine learning that trains layers of interconnected neurons for different applications. In which, convolution neural network (CNN) and its variations are typically used in image applications like classification and object detection. In this dissertation, we evaluated the application of deep learning on fashion analytics, especially fashion attribute annotation, using the trick of re-training final layers of existing state-of-the-art CNN models. We adopted the Inception V3 model and faster RCNN model with RESNET101 classifier to train the computer to classify fashion images and learn to detect objective and subjective fashion features. The evaluated classifier structures are the single labelled single CNN model, the multi labelled binary CNN models, the multi labelled single CNN model, the faster R-CNN model and a combined model of R-CNN with multi labelled CNN model. We evaluated the training efficiencies, sample requirement, training sample grouping, training accuracies and recall of the models. The faster R-CNN structure evaluated was typically applied on object/item detection, so we believe the application of R-CNN on local and global fashion style attribute detection is a novel attempt which achieved better results than conventional whole image approaches by CNNs in our study. We also discussed the application of the above models to build a machine for fashion scoring as well as application of the CNN model in a fashion design process. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022109836903411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 5.69 MB | Adobe PDF | View/Open |
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