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diff --git a/internal/ceres/canonical_views_clustering.h b/internal/ceres/canonical_views_clustering.h new file mode 100644 index 0000000..171ac55 --- /dev/null +++ b/internal/ceres/canonical_views_clustering.h @@ -0,0 +1,133 @@ +// Ceres Solver - A fast non-linear least squares minimizer +// Copyright 2010, 2011, 2012 Google Inc. All rights reserved. +// http://code.google.com/p/ceres-solver/ +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// * Neither the name of Google Inc. nor the names of its contributors may be +// used to endorse or promote products derived from this software without +// specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +// POSSIBILITY OF SUCH DAMAGE. +// +// 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 <glog/logging.h> +#include "ceres/collections_port.h" +#include "ceres/graph.h" +#include "ceres/map_util.h" +#include "ceres/internal/macros.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 Graph<int>& graph, + const CanonicalViewsClusteringOptions& options, + 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_ |