Visually Exploring Millions of Images using Image Maps and Graphs
Kai Uwe Barthel, Nico Hezel · 2019
This chapter describes different image sorting algorithms. It introduces a new measure, which is better suited to evaluate two-dimensional (2D) image arrangements. The chapter presents a modified self-sorting maps (SSM) algorithm with improved sorting quality and reduced complexity, which allows millions of images to be sorted very quickly. Graph-based approaches can handle changes in the image collection. The chapter also presents a new graph-based approach to visually browse very large sets of varying images. It shows how high-quality image features representing the image content can be generated using transformed activations of a convolutional neural network. The chapter then presents an overview of various visual browsing models for image exploration. A self-organizing map (SOM) is an artificial neural network that is trained using unsupervised learning to produce a lower dimensional, discrete representation of the input space, called a map. A SOM consists of components called nodes.