Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets

This type of approach ignores the structural information that can be observed at a high level. This structural information can for example concern spatial relationships

Once an approximate scale has been selected using this strategy, a robust estimator takes as input the potential one-to-one point assignments, com- putes the best transformation

As expected, the number of the wrongly added or removed edges in the approximate graphs decreases as the edge-based neighbourhood order increases.. Indeed, as more edges are

Therefore, the DFS strategy seems to correspond to a sim- ple and efficient approach for converting an optimal GM algo- rithm into an anytime one that offers a trade-off between

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It is well adapted to linking images in large collections, thanks to its higher efficiency over regular search techniques: Linking the im- ages of a million-sized image collection

In terms of the quadratic assignment problem, this learning algorithm amounts to (in a loose language) adjust- ing the node and edge compatibility functions in a way that the

6, which con- cerns K Kashima kernel on graphs (Eq. Whatever the kernel on walks, increasing the walk length decreases the performances. That can be explained by the fact that K