Unsupervised hierarchical image segmentation through fuzzy entropy maximization
作者:
Highlights:
• We present an unsupervised multilevel segmentation scheme for automatically segmenting grayscale and color images.
• Fuzzy 2-partition entropy is combined with Graph Cut to form a bi-level segmentation operator that splits a given region into 2 parts based on both global optimal threshold and local spatial coherence.
• A multilevel segmentation scheme iteratively performs on selected regions and color channels, producing a coarse-to-fine hierarchy of segments.
• The presented algorithm is evaluated using the Berkeley Segmentation Database and achieves competitive results compared with the state-of-the-art methods.
摘要
•We present an unsupervised multilevel segmentation scheme for automatically segmenting grayscale and color images.•Fuzzy 2-partition entropy is combined with Graph Cut to form a bi-level segmentation operator that splits a given region into 2 parts based on both global optimal threshold and local spatial coherence.•A multilevel segmentation scheme iteratively performs on selected regions and color channels, producing a coarse-to-fine hierarchy of segments.•The presented algorithm is evaluated using the Berkeley Segmentation Database and achieves competitive results compared with the state-of-the-art methods.
论文关键词:Image segmentation,Superpixel,Fuzzy partition,Graph cut
论文评审过程:Received 5 February 2016, Revised 5 March 2017, Accepted 6 March 2017, Available online 9 March 2017, Version of Record 30 March 2017.
论文官网地址:https://doi.org/10.1016/j.patcog.2017.03.012