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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, 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.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.12 December 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4378-4395

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

VISUAL ATTENTION
EVALUATION METRICS
SACCADIC MODELS
SCANPATH COMPARISON
LONG SHORT-TERM MEMORY NETWORK SEMANTIC HASHING

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.3002168

Pernyataan Tanggungjawab

Chen Xia, Junwei Han, Dingwen Zhang

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