Graph-based clustering for identifying region of interest in eye tracker data analysis

Localization of a viewer's region of interest (ROI) on eye gaze signal trajectories acquired by eye trackers is a widely used approach in scene analysis, image compression, and quality of experience assessment. In this paper, we propose a novel clustering approach for ROI estimation from potentially noisy raw eye gaze data, based on signal processing on graphs. The clustering approach adapts graph signal processing (GSP)-based classification by first cleverly selecting a starting data sample, and then classifying the remaining samples. Furthermore, Graph Fourier Transform is used to adjust GSP parameters on-the-fly to maximise accuracy. Experimental results show competitive clustering accuracy of our proposed scheme compared to Density-based spatial clustering of applications with noise (DB-SCAN), Distance-Threshold Identification (I-DT), and Mean-Shift on publicly available Shape Dataset and the potential of estimating ROI accurately on true eye tracker data