Combining Semantic and Visual Image Graphs for Efficient Search and Exploration of Large Dynamic Image Collections

Image collections today often consist of millions of images, making it impossible to get an overview of the entire content. In recent years, we have presented several demonstrators for graph-based systems allowing image search and a visual exploration of the collection. Meanwhile, very powerful visual and also joint visual-textual feature vectors have been developed, which are suitable for finding similar images to query images or according to a textual description. A drawback of these image feature vectors is that they have a high number of dimensions, which leads to long search times, especially for large image collections. In this paper, we show how it is possible to significantly reduce the search time even for high-dimensional feature vectors and improve the efficiency of the search system. By combining two different image graphs, on the one hand, an extremely fast approximate nearest neighbor search can be achieved. Experimental results show that the proposed method performs better than state-of-the-art methods. On the other hand, it is possible to visually explore the entire image collection in real time using a standard web browser. Unlike other graph-based search systems, the proposed image graphs can dynamically adapt to the insertion and removal of images from the collection.