A General Purpose Method for Image Collection Summarization and Exploration

We propose a flexible framework that can be used to explore large-scale image datasets and summarize photo albums. Our proposed method first groups images based on their semantic content, and then selects the most diverse and aesthetically pleasing images to represent each category. To ensure the selection of high-quality images, we use features extracted from a Convolutional Neural Network to assess their diversity and perceptual properties. The effectiveness of our method is tested using benchmarking datasets and a qualitative study.