Decoding of dynamic visual scenes from gaze features

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School of Science | Master's thesis
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Date

2012

Department

Tietotekniikan laitos

Major/Subject

Informaatiotekniikka

Mcode

T-61

Degree programme

Language

en

Pages

xi + 93

Series

Abstract

This thesis aims to examine the similarity across participants' eye-gaze behaviour and explore eye-gaze features that can effectively classify different dynamic visual scenes. Eye-gaze behaviour of 17 naíve participants was recorded while viewing 62 different dynamic visual scenes grouped into 4 categories (artificial patterns, naturalistic sceneries, sports, and directed movies). From the eye gaze, 15 features were extracted for each video clip viewed by participants. Three classification approaches (SVM, Adaboost SVM, and Naíve Bayes) were used to classify the eye-gaze data. The analysis showed that eye-gaze patterns carry characteristic features that can be used to categorize the 4 video categories. Subsequently, a filter approach (RMANOVA), and a wrapper approach (RFE) were used for feature selection to identify a subset of features that can increase the classification accuracy. The filter approach (RMANOVA) results suggest that 10 out of 15 features were statistically significant across different video categories while wrapper approach (RFE) revealed 12 features when combined with a naíve Bayes classifier as the induction algorithm in RFE. The classification accuracy with all 15 features was 54.96+-0.93% (mean +- S.E.M.). The average accuracy increased to 58.96+-1.68% when the subset of features given by filter approach was considered but showed no recognizable change for subset of features selected by wrapper approach (54.84+-0.96%). The results suggest that eye-movement features allow for a classification of different dynamic visual scenes. The subset of eye-gaze features selected by filter approach gives higher classification accuracy over wrapper approach.

Description

Supervisor

Kaski, Samuel

Thesis advisor

Ramkumar, Pavan
Pannasch, Sebastian

Keywords

eye tracking, gaze features, dynamic scenes, feature selection, eye movement, pattern classification, filter method, wrapper methods, RFE, RMANOVA, SVM, naive Bayes

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-2020122859141

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[dipl] Perustieteiden korkeakoulu / SCI