Deep Learning Techniques for Image Clustering and Classification
Zeynep Ünal, Yonis Gulzar · Advances in computational intelligence and robotics book series · 2025
This chapter explores deep learning techniques for image clustering and classification, crucial tasks in computer vision. It discusses unsupervised clustering methods and supervised classification approaches, including traditional methods like k-means and hierarchical clustering. The chapter also highlights the transformative impact of CNNs, ensemble methods, and transfer learning in classification. It uses case studies like fruit classification in agriculture and brain tumor clustering in medical imaging to illustrate the real-world applicability of these models. Challenges like data limitations, computational requirements, and explainability are also discussed. Future trends include self-supervised learning and multimodal models. The chapter aims to guide researchers and practitioners in leveraging deep learning for effective image clustering and classification across various domains.