Graph Navigation for Exploring Very Large Image Collections

Authors: Kai-Uwe Barthel, Nico Hezel

Abstract: We present a new approach to visually browse very large sets of untagged images. In this paper we describe how to generate high quality image descriptors/features using transformed activations of a convolutional neural network. These features are used to model image similarities, which again are used to build a hierarchical image graph. We show how such an image graph can be constructed efficiently. After investigating several browsing and visualization concepts, we found best user experience and ease of usage is achieved by projecting sub-graphs onto a regular 2D-image map. This allows users to explore the image graph similar to navigation services.