SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves
Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7438 |
| _version_ | 1797476879586820096 |
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| author | Robert Ahadizad Newport Carlo Russo Sidong Liu Abdulla Al Suman Antonio Di Ieva |
| author_facet | Robert Ahadizad Newport Carlo Russo Sidong Liu Abdulla Al Suman Antonio Di Ieva |
| author_sort | Robert Ahadizad Newport |
| collection | DOAJ |
| description | Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching “sequences of fixations over time” are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction. |
| first_indexed | 2024-03-09T21:10:01Z |
| format | Article |
| id | doaj.art-50f1343daa2a4fb995c119fb105e538b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:01Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-50f1343daa2a4fb995c119fb105e538b2023-11-23T21:49:10ZengMDPI AGSensors1424-82202022-09-012219743810.3390/s22197438SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal CurvesRobert Ahadizad Newport0Carlo Russo1Sidong Liu2Abdulla Al Suman3Antonio Di Ieva4Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, AustraliaComputational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, AustraliaFaculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, AustraliaFaculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, AustraliaFaculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, AustraliaRecent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching “sequences of fixations over time” are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction.https://www.mdpi.com/1424-8220/22/19/7438visual scanpathHilbert curvediscrete Fréchet distancecomputational neuroscienceeye-trackingfractal analysis |
| spellingShingle | Robert Ahadizad Newport Carlo Russo Sidong Liu Abdulla Al Suman Antonio Di Ieva SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves Sensors visual scanpath Hilbert curve discrete Fréchet distance computational neuroscience eye-tracking fractal analysis |
| title | SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves |
| title_full | SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves |
| title_fullStr | SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves |
| title_full_unstemmed | SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves |
| title_short | SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves |
| title_sort | softmatch comparing scanpaths using combinatorial spatio temporal sequences with fractal curves |
| topic | visual scanpath Hilbert curve discrete Fréchet distance computational neuroscience eye-tracking fractal analysis |
| url | https://www.mdpi.com/1424-8220/22/19/7438 |
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