Open AccessVisual perception and local features for foreground‐background segmentation
Author(s) -
Peng Tong,
He Kun,
Su Yao,
Hui Ziwei
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12434
Subject(s) - segmentation , artificial intelligence , computer science , scale space segmentation , image segmentation , computer vision , segmentation based object categorization , perception , pattern recognition (psychology) , pixel , divergence (linguistics) , linguistics , philosophy , neuroscience , biology
The traditional foreground‐background segmentation models mainly depend on the low‐level features of the image, while ignoring the visual effect. Combining visual perception and local features, a top‐down segmentation model is proposed. This model regards foreground‐background segmentation as a reasoning problem based on visual perception, and calculates the association between two‐pixel blocks through Kullback–Leibler divergence, which solves the ill‐posed problem of traditional single‐pixel recognition. Meanwhile, local features are used to optimize the overall segmentation results in detail and improve the segmentation accuracy. The experimental results on the CMU‐Cornell iCoseg database and the BSDS500 database show that visual perception and local features can help improve the segmentation performance to a certain extent.