Open AccessSalient object detection based on edge‐interior feature fusion
Author(s) -
Shi Yadi,
Qin Guihe,
Liang Yanhua,
Wang Xinchao,
Yan Jie,
Zhang Zhonghan
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/ipr2.12635
Subject(s) - computer science , artificial intelligence , feature (linguistics) , salient , pattern recognition (psychology) , benchmark (surveying) , decoupling (probability) , enhanced data rates for gsm evolution , boundary (topology) , edge detection , feature extraction , focus (optics) , computer vision , image (mathematics) , image processing , mathematics , engineering , philosophy , linguistics , mathematical analysis , physics , geodesy , optics , control engineering , geography
Recently, existing FCNs‐based methods have shown their advantages in processing object boundaries. However, these methods still suffer from false object interference, which appears in saliency predictions. To solve this problem, an edge‐interior feature fusion (EIFF) framework is proposed, which consists of an internal‐boundary decoupled generation structure with receptive field enlargement and attention mechanism enhancement, and a salient feature refinement module. Specifically, the framework first learns edge features and interior features through an internal‐boundary decoupling generation network, which is supervised by labels obtained by decoupling ground‐truth through an image erosion algorithm. Then, feature refinement module (FRM) is designed to purify the coarse prediction by focusing on the ambiguous regions through a mining strategy to generate the final saliency map. To compensate for shortcomings of the BCE and IU loss, we also introduce a weighted loss to guide our model to focus more on the error‐prone parts. Experimental results on five benchmark datasets demonstrate that the proposed method performs favorably against 19 state‐of‐the‐art approaches under four standard metrics.