Image clustering is a research hotspot in machine learning and computer vision. Existing graph-based semi-supervised deep clustering methods suffer from three problems: 1) because clustering uses only high-level features, the detailed information contained in shallow-level features is ignored; 2) most feature extraction networks employ the step odd convolutional kernel, which results in an uneven distribution of receptive field intensity; and 3) because the adjacency matrix is precomputed and fixed, it cannot adapt to changes in the relationship between samples. To solve the above problems, we propose a novel graph-based semi-supervised deep clustering method for image clustering. First, the parity cross-convolutional feature extraction and fusion module is used to extract high-quality image features. Then, the clustering constraint layer is designed to improve the clustering efficiency. And, the output layer is customized to achieve unsupervised regularization training. Finally, the adjacency matrix is inferred by actual network prediction. A graph-based regularization method is adopted for unsupervised training networks. Experimental results show that our method significantly outperforms state-of-the-art methods on USPS, MNIST, street view house numbers (SVHN), and fashion MNIST (FMNIST) datasets in terms of ACC, normalized mutual information (NMI), and ARI.

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.

Deep clustering aims to boost clustering performance by learning powerful representations via deep learning. Despite their superiority over conventional shallow algorithms, autoencoder-based methods are typically hindered by heavy dependencies on large datasets and computationally expensive pre-training phases. Moreover, they often struggle to learn representations that are sufficiently discriminative for complex clustering tasks. To bridge this gap, we introduce a novel discriminative clustering framework utilizing Siamese encoders. By jointly training a Siamese encoder and a discriminative learning module, our method simultaneously captures robust features from data augmentations and imposes intra-cluster compactness. This dual optimization yields highly discriminative representations, which obviates the necessity for pre-training while ensuring rapid convergence and high accuracy. Extensive experiments on multiple benchmarks validate the superiority of our approach over state-of-the-art baselines.