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14 August 2019 Fashion popularity analysis based on online social network via deep learning
Jiachen Zhong, Rita Tse, Gustavo Marfia, Giovanni Pau
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117938 (2019) https://doi.org/10.1117/12.2539771
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
In this paper, we provide an idea about how to utilize the deep neural network with large scale social network data to judge the quality of fashion images. Specifically, our aim is to build a deep neural network based model which is able to predict the popularity of fashion-related images. Convolutional Neural Network (CNN) and Multi-layer Perceptron (MLP) are the two major tools to construct the model architecture, in which the CNN is responsible for analyzing images and the MLP is responsible for analyzing other types of social network meta data. Based on this general idea, various tentative model structures are proposed, implemented, and compared in this research. To perform experiments, we constructed a fashion-related dataset which contains over 1 million records from the online social network. Though no real word prediction task has been tried yet, according to the result of dataset-based tests, our models demonstrate good abilities on predicting the popularity of fashion from the online social network using the Xception CNN. However, we also find a very interesting phenomenon, which intuitively indicates there may be limited correlation between popularity and visual design of a fashion due to the power and influence of the online social network.
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Jiachen Zhong, Rita Tse, Gustavo Marfia, and Giovanni Pau "Fashion popularity analysis based on online social network via deep learning", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117938 (14 August 2019); https://doi.org/10.1117/12.2539771
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KEYWORDS
Data modeling
Process modeling
Social networks
Feature extraction
Performance modeling
RGB color model
Convolution
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