Open AccessDENSE-GWP: AN IMPROVED PRIMARY VISUAL ENCODING MODEL BASED ON DENSE GABOR FEATURES
Author(s) -
Yibo Cui,
Chi Zhang,
Linyuan Wang,
Bin Yan,
Tong Li
Publication year - 2021
Publication title -
journal of mechanics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.236
H-Index - 30
eISSN - 1793-6810
pISSN - 0219-5194
DOI - 10.1142/s0219519421400170
Subject(s) - artificial intelligence , visual cortex , pattern recognition (psychology) , computer science , stimulus (psychology) , wavelet , voxel , gabor wavelet , perception , orientation (vector space) , functional magnetic resonance imaging , computer vision , visual perception , spatial frequency , human visual system model , wavelet transform , mathematics , neuroscience , psychology , discrete wavelet transform , cognitive psychology , physics , optics , geometry , image (mathematics)
Brain visual encoding models based on functional magnetic resonance imaging are growing increasingly popular. The Gabor wavelet pyramid model (GWP) is a classic example, exhibiting a good prediction performance for the primary visual cortex (V1, V2, and V3). However, the local variations in the visual stimulation are quite convoluted in terms of spatial frequency, orientation, and position, posing a challenge for visual encoding models. Whether the GWP model can thoroughly extract informative and effective features from visual stimulus remains unclear. To this end, this paper proposes a dense GWP visual encoding model by ameliorating the composition of the Gabor wavelet basis from three aspects: spatial frequency, orientation, and position. The improved model named Dense-GWP model could extract denser features from the image stimulus. A regularization optimization algorithm was used to select informative and effective features, which were crucial for predicting voxel activity in the region of interest. Extensive experimental results showed that the Dense-GWP model exhibits an improved prediction performance and can therefore help further understand the human visual perception mechanism.