Authors: Nico Hezel, Kai-Uwe Barthel, Klaus Jung
Abstract: In this paper we present a system to visually explore and search large sets of untagged images, running on common operating systems and consumer hardware. High quality image descriptors are computed using activations of a convolutional neural network. By applying normalization and a principal component analysis of the activations compact feature vectors of only 64 bytes are generated. The L1-distances for these feature vectors can be calculated very fast using a novel computation approach and allows search-by-example queries to be processed in fractions of a second. We further show how entire image collections can be transferred into hierarchical image graphs and describe a scheme to explore this complex data structure in an intuitive way. To enable keyword search for untagged images, reference features for common keywords are generated. These features are constructed by collecting and clustering examples images from the web.