| Summary: | Image clustering is a key and challenging task in the field of machine learning and computer vision. Technically, image clustering is the process of grouping images without the use of any supervisory information in order to retain similar images within the same cluster. This paper proposes a novel image clustering method based on coupled convolutional and graph convolutional network. It solves the problem that the deep clustering method usually only focuses on the useful features extracted from the sample itself, and seldom considers the structural information behind the sample. Experimental results show that the proposed algorithm can effectively extract more discriminative deep features, and the model achieves good clustering effect due to the combination of attribute information and structure information of samples in GCN.
|
|---|