Much of the real-world image data is unlabeled or mislabeled. Therefore, even if there is no label, if similar images can be grouped together with image data itself and used the group as a label, more image data can be effectively used in various tasks. This will be especially effective when dividing images belonging to the same domain into sub-groups. Therefore, in this study, we propose an image feature extraction method to be used for image clustering. The proposed feature extraction model is the Multi-head Convolutional Autoencoder (MCAE), which is a model composed of multiple encoders in parallel based on the Convolutional Autoencoder (CAE). The proposed model showed about 14% lower test reconstruction loss compared to CAE, and the correlation coefficient between extracted features was about 56% lower. In addition, as the results of clustering based on the extracted features, MCAE-based clustering showed about 3.5 times higher silhouette score than that CAE-based clustering.
ACKNOWLEDGMENT This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2C200347411). Also, this research was supported by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education (NRF5199991014091) and the Ajou University research fund.
