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- Title
Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images.
- Authors
Malhi, Umar Subhan; Zhou, Junfeng; Yan, Cairong; Rasool, Abdur; Siddeeq, Shahbaz; Du, Ming
- Abstract
Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate. Currently, popular methods for extracting features from data use deep learning techniques, such as a Convolutional Neural Network (CNN). These methods can generate high-dimensional feature vectors, which are effective for image clustering. However, high dimensions can lead to the curse of dimensionality, which makes subsequent clustering difficult. The fashion images-oriented deep clustering method (FIDC) is proposed in this paper. This method uses CNN to generate a 4096-dimensional feature vector for each fashion image through migration learning, then performs dimensionality reduction through a deep-stacked auto-encoder model, and finally performs clustering on these low-dimensional vectors. High-dimensional vectors can represent images, and dimensionality reduction avoids the curse of dimensionality during clustering tasks. A particular point in the method is the joint learning and optimization of the dimensionality reduction process and the clustering task. The optimization process is performed using two algorithms: back-propagation and stochastic gradient descent. The experimental findings show that the proposed method, called FIDC, has achieved state-of-the-art performance.
- Subjects
CONVOLUTIONAL neural networks
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 5, p2828
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13052828