Visually Browsing Millions of Images Using Image Graphs
Kai Uwe Barthel, Nico Hezel, Klaus Jung · 2017
We present a new approach to visually browse very large sets of untagged images. High quality image features are generated using transformed activations of a convolutional neural network. These features are used to model image similarities, from which a hierarchical image graph is build. We show how such a graph can be constructed efficiently. In our experiments we found best user experience for navigating the graph is achieved by projecting sub-graphs onto a regular 2D image map. This allows users to explore the image collection like an interactive map.