Edge-constraint based multi-scale contrastive learning for image deep clustering,

Jiawei Zhu, Xuegang Wu, Liu Yang,
Edge-constraint based multi-scale contrastive learning for image deep clustering,
Digital Signal Processing,
Volume 163,
2025,
105180,
ISSN 1051-2004,
https://doi.org/10.1016/j.dsp.2025.105180.
(https://www.sciencedirect.com/science/article/pii/S1051200425002027)
Abstract: Deep clustering algorithms using deep neural networks are crucial across various fields. Contrastive learning benefited by diverse data augmentations has proven effective in improving clustering performance. However, existing clustering methods based on contrastive learning primarily focus on similarities among augmented views of the same instance, often overlooking the rich semantic information inherent in the image itself. Therefore, this paper proposes an effective deep clustering method called Edge-Constraint based Multi-Scale Contrastive Learning for Image Deep Clustering. Based on the traditional two-channel paradigm, an edge channel is constructed to capture contour information and an edge constraint loss is generated for complementary contrastive learning by quantifying the structural similarity between edge image signals and augmented image signals from other channels. Additionally, a multi-scale feature enhancement module is proposed to improve the robustness of edge information extraction in a multi-scale environment. The experimental results show that the method proposed in this paper outperforms the current state-of-the-art clustering approaches in the field on the benchmarks of CIFAR-10, CIFAR-100, STL-10, ImageNet-10, and Tiny-ImageNet.
Keywords: Contrastive learning; Image clustering; Data augmentation; Edge feature