Dataset Fingerprints: Exploring Image Collections Through Data Mining

Konstantinos Rematas, Basura Fernando, Frank Dellaert, Tinne Tuytelaars; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4867-4875

As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content. In this paper, we propose dataset fingerprints, a new and powerful method based on data mining that extracts meaningful patterns from a set of images. The discovered patterns are compositions of discriminative mid-level features that co-occur in several images. Compared to earlier work, ours stands out because i) it's fully unsupervised, ii) discovered patterns cover large parts of the images,often corresponding to full objects or meaningful parts thereof, and iii) different patterns are connected based on co-occurrence, allowing a user to ``browse'' / ``surf'' the images from one pattern to the next and to group patterns in a semantically meaningful manner.