Navigating a Graph of Scenes for Exploring Large Video Collections

We present a novel approach to browse huge sets of video scenes using a hierarchical graph and visually sorted image maps allowing the user to explore the graph similar to navigation services. In a previous paper [1] we proposed a scheme to generate such a graph of video scenes and investigated several browsing and visualization concepts. In this paper we extend our work by adding semantic features learned from a convolutional neural network. In combination with visual features we constructed an improved graph where related images (video scenes) are connected with each other. Different images or areas in the graph may be reached by following the most promising path of edges. For efficient navigation we propose a method which projects images onto a 2D plane preserving their complex inter-image relationships. To start a search process, the user may either choose from a selection of typical videos scenes or use tools such as search by sketch or category. The retrieved video frames are arranged on a canvas and the view of the graph is directed to a location where matching frames can be found.