Image Similarity

Image similarity research focuses on developing methods to quantitatively measure the resemblance between images, crucial for various computer vision tasks like retrieval, recognition, and quality assessment. Current research emphasizes efficient and robust similarity metrics, exploring architectures like transformers and Siamese networks, and incorporating contextual information to address the inherent ambiguity of visual similarity. These advancements are driving improvements in applications ranging from medical imaging analysis and 3D reconstruction to visual search and content-adaptive rendering, impacting both scientific understanding of visual perception and the performance of real-world systems.