Real-Time Visual Navigation in Huge Image Sets Using Similarity Graphs
This addresses the challenge for users like stock photo agencies to efficiently navigate and gain overviews of massive image sets, though it builds incrementally on prior graph-based methods.
The paper tackles the problem of visually exploring huge image collections by introducing a real-time navigation system that uses hierarchical similarity graphs to display subsets as a zoomable 2D map, enabling users to browse millions of images interactively without overwhelming lists.
Nowadays stock photo agencies often have millions of images. Non-stop viewing of 20 million images at a speed of 10 images per second would take more than three weeks. This demonstrates the impossibility to inspect all images and the difficulty to get an overview of the entire collection. Although there has been a lot of effort to improve visual image search, there is little research and support for visual image exploration. Typically, users start "exploring" an image collection with a keyword search or an example image for a similarity search. Both searches lead to long unstructured lists of result images. In earlier publications, we introduced the idea of graph-based image navigation and proposed an efficient algorithm for building hierarchical image similarity graphs for dynamically changing image collections. In this demo we showcase real-time visual exploration of millions of images with a standard web browser. Subsets of images are successively retrieved from the graph and displayed as a visually sorted 2D image map, which can be zoomed and dragged to explore related concepts. Maintaining the positions of previously shown images creates the impression of an "endless map". This approach allows an easy visual image-based navigation, while preserving the complex image relationships of the graph.