Subset based deep learning for RGB-D object recognition

Neurocomputing

Volumn 165, Issue , 2015, Pages 280-292

  Bai, Jing ^a   Wu, Yan ^a   Zhang, Junming ^a   Chen, Fuqiang ^a  

^a TONGJI UNIVERSITY   (China)

Author keywords

Deep learning; Recursive neural networks; RGB D object recognition; Sparse auto encoder; Subset based feature extracting

Indexed keywords

DEEP NEURAL NETWORKS; IMAGE ENHANCEMENT; LEARNING SYSTEMS; NEURAL NETWORKS; OBJECT RECOGNITION; SET THEORY; SIGNAL ENCODING;

AUTO ENCODERS; COLOR AND DEPTH IMAGES; FEATURE EXTRACTING; FEATURE REPRESENTATION; HIERARCHICAL FEATURES; RECOGNITION ACCURACY; RECURSIVE NEURAL NETWORKS; STATE-OF-THE-ART PERFORMANCE;

DEEP LEARNING;

ACCURACY; ARTICLE; CAMERA; COLOR; COMPARATIVE STUDY; CONTROLLED STUDY; DATA PROCESSING; DYNAMICS; ERROR; IMAGE ANALYSIS; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; NERVE CELL NETWORK; OBJECT RECOGNITION; PHASE TRANSITION; PRIORITY JOURNAL; QUALITY CONTROL; RECOGNITION; RECURSIVE NEURAL NETWORK; SHAPE; SUBSET BASED DEEP LEARNING;

EID: 84929954951     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.03.017     Document Type: Article

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