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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
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// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// An implementation of the Canonical Views clustering algorithm from
// "Scene Summarization for Online Image Collections", Ian Simon, Noah
// Snavely, Steven M. Seitz, ICCV 2007.
//
// More details can be found at
// http://grail.cs.washington.edu/projects/canonview/
//
// Ceres uses this algorithm to perform view clustering for
// constructing visibility based preconditioners.
#ifndef CERES_INTERNAL_CANONICAL_VIEWS_CLUSTERING_H_
#define CERES_INTERNAL_CANONICAL_VIEWS_CLUSTERING_H_
#include <vector>
#include "ceres/collections_port.h"
#include "ceres/graph.h"
namespace ceres {
namespace internal {
struct CanonicalViewsClusteringOptions;
// Compute a partitioning of the vertices of the graph using the
// canonical views clustering algorithm.
//
// In the following we will use the terms vertices and views
// interchangably. Given a weighted Graph G(V,E), the canonical views
// of G are the the set of vertices that best "summarize" the content
// of the graph. If w_ij i s the weight connecting the vertex i to
// vertex j, and C is the set of canonical views. Then the objective
// of the canonical views algorithm is
//
// E[C] = sum_[i in V] max_[j in C] w_ij
// - size_penalty_weight * |C|
// - similarity_penalty_weight * sum_[i in C, j in C, j > i] w_ij
//
// alpha is the size penalty that penalizes large number of canonical
// views.
//
// beta is the similarity penalty that penalizes canonical views that
// are too similar to other canonical views.
//
// Thus the canonical views algorithm tries to find a canonical view
// for each vertex in the graph which best explains it, while trying
// to minimize the number of canonical views and the overlap between
// them.
//
// We further augment the above objective function by allowing for per
// vertex weights, higher weights indicating a higher preference for
// being chosen as a canonical view. Thus if w_i is the vertex weight
// for vertex i, the objective function is then
//
// E[C] = sum_[i in V] max_[j in C] w_ij
// - size_penalty_weight * |C|
// - similarity_penalty_weight * sum_[i in C, j in C, j > i] w_ij
// + view_score_weight * sum_[i in C] w_i
//
// centers will contain the vertices that are the identified
// as the canonical views/cluster centers, and membership is a map
// from vertices to cluster_ids. The i^th cluster center corresponds
// to the i^th cluster.
//
// It is possible depending on the configuration of the clustering
// algorithm that some of the vertices may not be assigned to any
// cluster. In this case they are assigned to a cluster with id = -1;
void ComputeCanonicalViewsClustering(
const CanonicalViewsClusteringOptions& options,
const WeightedGraph<int>& graph,
std::vector<int>* centers,
HashMap<int, int>* membership);
struct CanonicalViewsClusteringOptions {
CanonicalViewsClusteringOptions()
: min_views(3),
size_penalty_weight(5.75),
similarity_penalty_weight(100.0),
view_score_weight(0.0) {
}
// The minimum number of canonical views to compute.
int min_views;
// Penalty weight for the number of canonical views. A higher
// number will result in fewer canonical views.
double size_penalty_weight;
// Penalty weight for the diversity (orthogonality) of the
// canonical views. A higher number will encourage less similar
// canonical views.
double similarity_penalty_weight;
// Weight for per-view scores. Lower weight places less
// confidence in the view scores.
double view_score_weight;
};
} // namespace internal
} // namespace ceres
#endif // CERES_INTERNAL_CANONICAL_VIEWS_CLUSTERING_H_