Visual Similarity

Visual similarity research focuses on developing methods to quantify and understand how similar images or objects appear, a crucial task with applications ranging from e-commerce to medical image analysis. Current research employs deep learning architectures, such as variations of ResNet and other convolutional neural networks, to extract features for similarity comparisons using metrics like cosine similarity and Euclidean distance, and also explores generative AI for image compression while preserving perceptual similarity. This field is vital for improving image retrieval, facilitating more effective human-computer interaction, and advancing the development of robust and efficient image processing and communication systems.