A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback

IEEE Transactions on Information Technology in Biomedicine

Volumn 15, Issue 4, 2011, Pages 640-646

  Rahman, Md Mahmudur ^a   Antani, Sameer K ^a   Thoma, George R ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Classification; classifier combination; contentbased image retrieval (CBIR); medical imaging; relevance feedback (RF); similarity fusion

Indexed keywords

BIOMEDICAL IMAGES; BODY PARTS; CLASSIFIER COMBINATION; COMPUTATION TIME; CONTENT-BASED IMAGE RETRIEVAL; DATABASE IMAGES; FEATURE REPRESENTATION; FEED BACK INFORMATION; GLOBAL LEVELS; IMAGE FILTERING; LINEAR COMBINATIONS; MULTICLASS SUPPORT VECTOR MACHINES; PROBABILISTIC OUTPUT; QUERY IMAGES; RANK ORDER; RELEVANCE FEEDBACK; RETRIEVAL FRAMEWORKS; RETRIEVED IMAGES; SEARCH SPACES; SEMANTIC INTERPRETATION; SIMILARITY FUSION; SIMILARITY-MATCHING; SVM CLASSIFICATION; UNIQUE FEATURES;

FEEDBACK; FORECASTING; IMAGE FUSION; IMAGE RETRIEVAL; SEMANTICS; SUPERVISED LEARNING; VECTOR SPACES;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; DIAGNOSTIC IMAGING; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED;

EID: 79960504796     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2011.2151258     Document Type: Article

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