Supervised and unsupervised parallel subspace learning for large-scale image recognition

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 22, Issue 10, 2012, Pages 1497-1511

  Jing, Xiao Yuan ^a,b,c   Li, Sheng ^b   Zhang, David ^d   Yang, Jian ^e   Yang, Jing Yu ^e  

^a WUHAN UNIVERSITY   (China)

^b NANJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c NANJING UNIVERSITY   (China)

^d HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^e NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Feature selection; graph embedding; large scale image recognition; parallel linear discriminant analysis (PLDA); parallel locality preserving projection (PLPP); parallel subspace learning framework

Indexed keywords

AFFINITY MATRIX; COMPUTATIONAL ABILITY; COMPUTATIONAL BURDEN; COMPUTATIONAL TIME; DATA DIVISION; FISHER SCORE; GRAPH EMBEDDINGS; IMAGE FEATURE EXTRACTIONS; LAPLACIANS; LINEAR DISCRIMINANT ANALYSIS; LOCALITY PRESERVING PROJECTIONS; PARALLEL-COMPUTING ENVIRONMENT; PROJECTION VECTORS; PUBLIC IMAGE; RANDOM DATA; REAL-WORLD APPLICATION; RECOGNITION PERFORMANCE; SAMPLE SETS; SUBSPACE LEARNING; SUBTASKS; TEST DATA; TIME COMPLEXITY; VIDEO DATABASE;

FACE RECOGNITION; FEATURE EXTRACTION; LEARNING SYSTEMS; PARALLEL ARCHITECTURES;

VECTORS;

EID: 84867066922     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2012.2202079     Document Type: Article

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