Graph Navigation for Exploring Very Large Image Collections
12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2017)
TL;DR
We describe a method for generating high-quality image descriptors from convolutional neural network activations, which are used to model image similarities and build an efficient hierarchical image graph. Projecting sub-graphs onto a 2D image map provides the best user experience and ease of use.
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.
BibTeX
If you use our work in your research, please cite our publication:
@conference{visapp17,
author={Kai Uwe Barthel. and Nico Hezel.},
title={Graph Navigation for Exploring Very Large Image Collections},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP},
year={2017},
pages={411-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006274804110416},
isbn={978-989-758-226-4},
issn={2184-4321},
}