ScatterNet: A Deep Subjective Similarity Model for Visual Analysis of Scatterplots

TLDR

Similarity measuring methods are widely adopted in a broad range of visualization applications. This work introduces ScatterNet, a deep‑learning approach that models human perception of scatterplot similarity. ScatterNet extracts semantic features from scatterplot images using deep neural networks trained on a large labeled dataset of similar and dissimilar plots, and its performance is validated through experiments and a user study. Evaluations confirm that the learned features capture human perception of scatterplot similarity effectively, and ScatterNet can be applied in visual analysis scenarios.

Abstract

Similarity measuring methods are widely adopted in a broad range of visualization applications. In this work, we address the challenge of representing human perception in the visual analysis of scatterplots by introducing a novel deep-learning-based approach, ScatterNet, captures perception-driven similarities of such plots. The approach exploits deep neural networks to extract semantic features of scatterplot images for similarity calculation. We create a large labeled dataset consisting of similar and dissimilar images of scatterplots to train the deep neural network. We conduct a set of evaluations including performance experiments and a user study to demonstrate the effectiveness and efficiency of our approach. The evaluations confirm that the learned features capture the human perception of scatterplot similarity effectively. We describe two scenarios to show how ScatterNet can be applied in visual analysis applications.