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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

Abstract

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.
LanguageEnglish
Title of host publication2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages6
ISBN (Print)9781509036509
DOIs
Publication statusPublished - 1 Dec 2017
Event19th IEEE International Workshop on Multimedia Signal Processing - Luton, United Kingdom
Duration: 16 Oct 201718 Oct 2017
https://www.beds.ac.uk/mmsp2017

Workshop

Workshop19th IEEE International Workshop on Multimedia Signal Processing
Abbreviated titleMMSP 2017
CountryUnited Kingdom
CityLuton
Period16/10/1718/10/17
Internet address

Fingerprint

Signal processing
Image compression
Image quality
Fourier transforms
Trajectories

Keywords

  • noise measurement
  • robustness
  • clustering algorithms
  • signal processing
  • clustering methods
  • Laplace equations
  • Fourier transforms

Cite this

He, K., Yang, C., Stankovic, V., & Stankovic, L. (2017). Graph-based clustering for identifying region of interest in eye tracker data analysis. In 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP) Piscataway, NJ.: IEEE. https://doi.org/10.1109/MMSP.2017.8122264
He, Kanghang ; Yang, Cheng ; Stankovic, Vladimir ; Stankovic, Lina. / Graph-based clustering for identifying region of interest in eye tracker data analysis. 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). Piscataway, NJ. : IEEE, 2017.
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title = "Graph-based clustering for identifying region of interest in eye tracker data analysis",
abstract = "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.",
keywords = "noise measurement, robustness, clustering algorithms, signal processing, clustering methods, Laplace equations, Fourier transforms",
author = "Kanghang He and Cheng Yang and Vladimir Stankovic and Lina Stankovic",
note = "{\circledC} 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
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He, K, Yang, C, Stankovic, V & Stankovic, L 2017, Graph-based clustering for identifying region of interest in eye tracker data analysis. in 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). IEEE, Piscataway, NJ., 19th IEEE International Workshop on Multimedia Signal Processing, Luton, United Kingdom, 16/10/17. https://doi.org/10.1109/MMSP.2017.8122264

Graph-based clustering for identifying region of interest in eye tracker data analysis. / He, Kanghang; Yang, Cheng; Stankovic, Vladimir; Stankovic, Lina.

2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). Piscataway, NJ. : IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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.

AB - 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.

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He K, Yang C, Stankovic V, Stankovic L. Graph-based clustering for identifying region of interest in eye tracker data analysis. In 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). Piscataway, NJ.: IEEE. 2017 https://doi.org/10.1109/MMSP.2017.8122264