Learning kernels for variants of normalized cuts: Convex relaxations and applications

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 3145-3152

  Mukherjee, Lopamudra ^a   Singh, Vikas ^b   Peng, Jiming ^c   Hinrichs, Chris ^b  

^a UNIVERSITY OF WISCONSIN WHITEWATER   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASIS SETS; BRAIN IMAGING; CONVEX RELAXATION; DATA SETS; FEATURE TYPES; KERNEL WEIGHT; LEARNING KERNELS; LEARNING PROBLEM; NORMALIZED CUTS; OPTIMALITY; PROBLEM SIZE; QUALITY OF SOLUTION; SALIENT FEATURES; SDP RELAXATION; SIMILARITY FUNCTIONS; TRAINING EXAMPLE; TRAINING SETS;

COMPUTER VISION; RELAXATION PROCESSES;

IMAGE SEGMENTATION;

EID: 77956000518     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540076     Document Type: Conference Paper

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