In the past an efficient and satisfactory image search was only possible by using a combation of keywords and low-level visual image features. Recently Convolutional Neural Networks (CNNs) have enabled automatic understanding of images. This results in a multitude of new applications and improved visual image search systems. This talk provides an overview of the different methods for image search, gives an overview of the principle of CNNs and shows how future image search systems could look like. We present a new approach to visually explore very large sets of untagged images. High quality image descriptors are generated using transformed activations of a convolutional neural network. These features are used to model image similarities, from which a hierarchical image graph is build. We show how such a graph can be constructed efficiently. Best user experience for navigating this graph is achieved by projecting sub-graphs onto a regular 2D-image map. This allows users to explore the image graph similar to navigation services.
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