Semantic Image Clustering with Deep Learning

Image clustering is a crucial but open and challenging task in machine learning and computer vision. Deep image clustering methods have made significant advancements in largescale and high-dimensional image datasets, but they only explore clusters according to feature similarity. How to improve the semantic plausibility of these clusters remains a challenging problem. To address this problem, we propose a joint semantic image clustering (SIC) with deep learning framework. Our key idea is to explore semantic clusters from both instance-level and clusterlevel perspectives. At the cluster level, we assume that the highest-confidence partitions possess reasonable semantic divisions; at the instance level, we assume that neighboring samples belong to the same semantic category. With the constraints of the cluster level and instance level, the intracluster compactness and interclass discrepancy are increased. Clustering results heavily rely on the quality of image representations. Therefore, we introduce strong data augmentations with three shared-weight backbone networks to learn the most inherent features. The experimental results on various image datasets demonstrate our framework’s superiority over a wide range of state-of-the-art approaches.