Evaluation of Saccadic Scanpath Prediction: Subjective Assessment Database and Recurrent Neural Network Based Metric

In recent years, predicting the saccadic scanpaths of humans has become a new trend in the field of visual attention modeling. Given various saccadic algorithms, determining how to evaluate their ability to model a dynamic saccade has become an important yet understudied issue. To our best knowledge...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 43; číslo 12; s. 4378 - 4395
Hlavní autoři: Xia, Chen, Han, Junwei, Zhang, Dingwen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Computational modeling
Evaluation
evaluation metrics
Feature extraction
Long Short-Term Memory (LSTM) network
Measurement
Prediction models
Predictive models
Recurrent neural networks
saccadic models
scanpath comparison
semantic hashing
Subjective assessment
Visual attention
Visual databases
Visual fields
Visual perception
Visual perception driven algorithms
Visualization
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:In recent years, predicting the saccadic scanpaths of humans has become a new trend in the field of visual attention modeling. Given various saccadic algorithms, determining how to evaluate their ability to model a dynamic saccade has become an important yet understudied issue. To our best knowledge, existing metrics for evaluating saccadic prediction models are often heuristically designed, which may produce results that are inconsistent with human subjective assessment. To this end, we first construct a subjective database by collecting the assessments on 5,000 pairs of scanpaths from ten subjects. Based on this database, we can compare different metrics according to their consistency with human visual perception. In addition, we also propose a data-driven metric to measure scanpath similarity based on the human subjective comparison. To achieve this goal, we employ a long short-term memory (LSTM) network to learn the inference from the relationship of encoded scanpaths to a binary measurement. Experimental results have demonstrated that the LSTM-based metric outperforms other existing metrics. Moreover, we believe the constructed database can be used as a benchmark to inspire more insights for future metric selection.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.3002168