Real-Time Visual Navigation in Huge Image Sets Using Similarity Graphs

TL;DR

This paper presents a real-time, graph-based visual navigation system enabling users to explore millions of images efficiently through an interactive 2D map, enhancing image collection overview and exploration.

Contribution

It introduces a novel real-time image exploration method using hierarchical similarity graphs displayed as an interactive 2D map, supporting dynamic large-scale image collections.

Findings

Enables real-time browsing of millions of images.

Provides an interactive, zoomable image map.

Maintains image relationships for intuitive exploration.

Abstract

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.