Improving Semantic Image Segmentation With a Probabilistic Superpixel-Based Dense Conditional Random Field.
Liang ZhangHuan LiPeiyi ShenGuangming ZhuJuan SongSyed Afaq Ali ShahMohammed BennamounLi ZhangPublished in: IEEE Access (2018)
Keyphrases
- conditional random fields
- image segmentation
- probabilistic model
- superpixels
- markov random field
- generative model
- semantic segmentation
- image segmentation algorithm
- segmentation method
- graphical models
- long range
- random fields
- crf model
- expectation maximization
- maximum entropy
- higher order
- graph cuts
- object segmentation
- structured prediction
- image labeling
- multiscale
- parameter estimation
- hidden markov models
- information extraction
- segmentation algorithm
- pairwise
- bayesian networks
- image processing
- semantic similarity
- belief networks
- active contours
- max margin
- em algorithm
- undirected graphical models
- mean shift
- markov networks
- approximate inference
- shape prior
- posterior probability
- belief propagation
- deformable models
- data mining
- information retrieval