Research on Deep Clustering Based on Image Data

Clustering is an important branch of unsupervised tasks, aiming at mining deeper relationships and patterns in data. The quality of feature representation based on image datasets often determines the upper limit of clustering tasks. In recent years, the application of deep learning in deep clustering representation learning module has learned better feature representation, which gradually overcomes the limitation of traditional shallow clustering facing high-dimensional unstructured data. Deep clustering has become a popular research direction in the unsupervised field in recent years, and high clustering performance has been obtained in the continuous deepening research. The existing deep clustering research is mainly oriented to various fields of artificial intelligence, including natural language processing (NLP), speech recognition (ASR), computer vision (CV), etc. We take the field of computer vision as the entry point, analyze and study the research progress of deep clustering in computer vision (CV), and deconstruct the deep clustering model into two parts: a detailed classification of the feature extraction module from the perspective of network model architecture, while the data space of clustering is used as the entry point to divide the clustering module, and discuss the deep clustering. Finally, we analyze the datasets and evaluation metrics commonly used in experiments to study deep clustering architectures in computer vision.