High-Dimensional Data Visualisation Methods Using Machine Learning and Their Use in Image Analysis

The purpose of this research is to investigate the use of information visualisation in deep learning and big data analytics.First, we present a notion that is crucial to the production of visualisations: visualisation repositioning.We can relocate visualisations and extract data from static photos using a pattern recognition-based approach, which frees up more time for artists to create and lessens the workload of designers.Next, we present VisCode, an application that may be used to encode unprocessed data into static images for superior visualisation repositioning.VisCode offers two sorts of repositioning: thematic (which applies several colour themes for the user to select) and representational (which encodes raw data and visualisation types into static visualisation graphics in JSON format).In order to demonstrate VisCode's potential utility and usefulness in the information visualisation space, we conclude with a sample application that involves moving a colour density map visualisation.