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Supervised Learning of Graph Structure
Published on Oct 17, 20113713 Views
Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available
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Chapter list
Supervised Learning of Graph Structure00:00
Graph-Based Representations00:10
Difficulties in Graph Learning01:07
Generative Graph Model - 102:24
Generative Graph Model - 203:17
External Nodes Generation04:19
Probability of Observing a Graph05:23
Estimatimg the Correspondences06:18
Overcoming the Bias07:03
Correspondence Sampler07:33
Updating the Model07:55
Learning the Model08:55
Minimum Message Length Criterion10:12
Modeling the Attributes11:12
Shock Graphs12:20
Shock Graphs: Distance Matrix13:36
Shock Graphs: Precision and Recall13:59
3D Shapes14:26
3D Shapes: Distance Matrix14:49
3D Shapes: Precision and Recall15:05
COIL-2015:42
COIL-20: Distance Matrix16:24
COIL-20: Precision and Recall16:48
Synthetic Data: Distance Matrix17:17
Synthetic Data: Precision and Recall17:55
Conclusions18:19
References19:28