Abstract
In many visual domains like fashion, building an effective unsupervised clustering model depends on visual feature representation instead of structured and semi-structured data. In this paper, we propose a fashion image deep clustering (FiDC) model which includes two parts, feature representation and clustering. The fashion images are used as the input and are processed by a deep stacked autoencoder to produce latent feature representation, and the output of this autoencoder will be used as the input of the clustering task. Since the output of the former has a great influence on the later, the strategy adopted in the model is to integrate the learning process of the autoencoder and the clustering together. The autoencoder is trained with the optimal number of neurons per hidden layers to avoid overfitting and we optimize the cluster centroid by using stochastic gradient descent and backpropagation algorithm. We evaluate FiDC model on a real-world fashion dataset downloaded from Amazon where images have been extracted into 4096-dimensional visual feature vectors by convolutional neural networks. The experimental results show that our model achieves state-of-the-art performance.
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This research was partly funded by the National Natural Science Foundation of China (No. 61402100).
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Yan, C., Malhi, U.S., Huang, Y., Tao, R. (2019). Unsupervised Deep Clustering for Fashion Images. In: Uden, L., Ting, IH., Corchado, J. (eds) Knowledge Management in Organizations. KMO 2019. Communications in Computer and Information Science, vol 1027. Springer, Cham. https://doi.org/10.1007/978-3-030-21451-7_8
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