1 research outputs found
Real-Time Visual Navigation in Huge Image Sets Using Similarity Graphs
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