Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features

IEEE Transactions on Information Technology in Biomedicine

Volumn 16, Issue 1, 2012, Pages 98-105

  Shen, Yao ^a   Guturu, Parthasarathy Partha ^a   Buckles, Bill P ^a  

^a UNIVERSITY OF NORTH TEXAS   (United States)

Author keywords

Classification; Probabilistic latent semantic analysis; Scale invariant feature transform; Video segmentation; Wireless capsule endoscopy

Indexed keywords

CLASSIFICATION ACCURACY; CONTENT ANALYSIS; DATA CLUSTERING; DIGESTIVE TRACT; IMAGE FEATURES; LARGE DATABASE; LARGE INTESTINE; PROBABILISTIC LATENT SEMANTIC ANALYSIS; SCALE INVARIANT; SCALE INVARIANT FEATURE TRANSFORMS; SMALL INTESTINE; SUPERVISED CLASSIFICATION; TRAINING DATA SETS; TRAINING SAMPLE; VIDEO SEGMENTATION; WIRELESS CAPSULE ENDOSCOPY;

CLUSTERING ALGORITHMS; DIGESTIVE SYSTEM; ENDOSCOPY; IMAGE SEGMENTATION; UNSUPERVISED LEARNING;

CLASSIFICATION (OF INFORMATION);

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CAPSULE ENDOSCOPY; CLUSTER ANALYSIS; HUMAN; IMAGE PROCESSING; METHODOLOGY; SEMANTICS; VIDEORECORDING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CAPSULE ENDOSCOPY; CLUSTER ANALYSIS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; SEMANTICS; VIDEOTAPE RECORDING;

EID: 84856885561     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2011.2171977     Document Type: Review

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