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A Hybrid Autoencoder Network for Unsupervised Image Clustering

Department of Computer Science & Information Management, SooChow University, No.56 Kueiyang Street, Section 1, Taipei 100, Taiwan
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Algorithms 2019, 12(6), 122; https://doi.org/10.3390/a12060122
Received: 29 April 2019 / Revised: 4 June 2019 / Accepted: 13 June 2019 / Published: 15 June 2019
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Abstract

Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k-means or the agglomerative clustering method, have been widely used for the task of clustering, it is difficult for them to handle image data due to having no predefined distance metrics and high dimensionality. Recently, deep unsupervised feature learning methods, such as the autoencoder (AE), have been employed for image clustering with great success. However, each model has its specialty and advantages for image clustering. Hence, we combine three AE-based models—the convolutional autoencoder (CAE), adversarial autoencoder (AAE), and stacked autoencoder (SAE)—to form a hybrid autoencoder (BAE) model for image clustering. The MNIST and CIFAR-10 datasets are used to test the result of the proposed models and compare the results with others. The results of the clustering criteria indicate that the proposed models outperform others in the numerical experiment. View Full-Text
Keywords: image clustering; convolutional autoencoder (CAE); adversarial autoencoder (AAE); stacked autoencoder (SAE) image clustering; convolutional autoencoder (CAE); adversarial autoencoder (AAE); stacked autoencoder (SAE)
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Chen, P.-Y.; Huang, J.-J. A Hybrid Autoencoder Network for Unsupervised Image Clustering. Algorithms 2019, 12, 122.

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