Visual berrypicking in large image collections

Abstract

Exploring image collections using similarity-based two-dimensional maps is an ongoing research area that faces two main challenges: with increasing size of the collection and complexity of the similarity metric projection accuracy rapidly degrades and computational costs prevent online map generation. We propose a prototype that creates the impression of panning a large (global) map by aligning inexpensive small maps showing local neighborhoods. By directed hopping from one neighborhood to the next the user is able to explore the whole image collection. Additionally, the similarity metric can be adapted by weighting image features and thus users benefit from a more informed navigation.