Analyzing training information from random forests for improved image segmentation

IEEE Transactions on Image Processing

Volumn 23, Issue 4, 2014, Pages 1504-1512

  Mahapatra, Dwarikanath ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

context; feature selection; graph cut; probability maps; Random forests; segmentation; supervoxels; training information

Indexed keywords

CONTEXT; GRAPH CUT; PROBABILITY MAPS; RANDOM FORESTS; SUPERVOXELS;

DECISION TREES; FEATURE EXTRACTION; GRAPHIC METHODS; MEDICAL IMAGE PROCESSING;

IMAGE SEGMENTATION;

ALGORITHM; DECISION TREE; HUMAN; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES; STATISTICAL MODEL;

ALGORITHMS; DECISION TREES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL;

EID: 84896747792     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2305073     Document Type: Article

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