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diff --git a/internal/ceres/canonical_views_clustering_test.cc b/internal/ceres/canonical_views_clustering_test.cc
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+// 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: Sameer Agarwal (sameeragarwal@google.com)
+//         David Gallup (dgallup@google.com)
+
+#include "ceres/canonical_views_clustering.h"
+
+#include "ceres/collections_port.h"
+#include "ceres/graph.h"
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+const int kVertexIds[] = {0, 1, 2, 3};
+class CanonicalViewsTest : public ::testing::Test {
+ protected:
+  virtual void SetUp() {
+    // The graph structure is as follows.
+    //
+    // Vertex weights:   0      2      2      0
+    //                   V0-----V1-----V2-----V3
+    // Edge weights:        0.8    0.9    0.3
+    const double kVertexWeights[] = {0.0, 2.0, 2.0, -1.0};
+    for (int i = 0; i < 4; ++i) {
+      graph_.AddVertex(i, kVertexWeights[i]);
+    }
+    // Create self edges.
+    // CanonicalViews requires that every view "sees" itself.
+    for (int i = 0; i < 4; ++i) {
+      graph_.AddEdge(i, i, 1.0);
+    }
+
+    // Create three edges.
+    const double kEdgeWeights[] = {0.8, 0.9, 0.3};
+    for (int i = 0; i < 3; ++i) {
+      // The graph interface is directed, so remember to create both
+      // edges.
+      graph_.AddEdge(kVertexIds[i], kVertexIds[i + 1], kEdgeWeights[i]);
+    }
+  }
+
+  void ComputeClustering() {
+    ComputeCanonicalViewsClustering(graph_, options_, &centers_, &membership_);
+  }
+
+  Graph<int> graph_;
+
+  CanonicalViewsClusteringOptions options_;
+  vector<int> centers_;
+  HashMap<int, int> membership_;
+};
+
+TEST_F(CanonicalViewsTest, ComputeCanonicalViewsTest) {
+  options_.min_views = 0;
+  options_.size_penalty_weight = 0.5;
+  options_.similarity_penalty_weight = 0.0;
+  options_.view_score_weight = 0.0;
+  ComputeClustering();
+
+  // 2 canonical views.
+  EXPECT_EQ(centers_.size(), 2);
+  EXPECT_EQ(centers_[0], kVertexIds[1]);
+  EXPECT_EQ(centers_[1], kVertexIds[3]);
+
+  // Check cluster membership.
+  EXPECT_EQ(FindOrDie(membership_, kVertexIds[0]), 0);
+  EXPECT_EQ(FindOrDie(membership_, kVertexIds[1]), 0);
+  EXPECT_EQ(FindOrDie(membership_, kVertexIds[2]), 0);
+  EXPECT_EQ(FindOrDie(membership_, kVertexIds[3]), 1);
+}
+
+// Increases size penalty so the second canonical view won't be
+// chosen.
+TEST_F(CanonicalViewsTest, SizePenaltyTest) {
+  options_.min_views = 0;
+  options_.size_penalty_weight = 2.0;
+  options_.similarity_penalty_weight = 0.0;
+  options_.view_score_weight = 0.0;
+  ComputeClustering();
+
+  // 1 canonical view.
+  EXPECT_EQ(centers_.size(), 1);
+  EXPECT_EQ(centers_[0], kVertexIds[1]);
+}
+
+
+// Increases view score weight so vertex 2 will be chosen.
+TEST_F(CanonicalViewsTest, ViewScoreTest) {
+  options_.min_views = 0;
+  options_.size_penalty_weight = 0.5;
+  options_.similarity_penalty_weight = 0.0;
+  options_.view_score_weight = 1.0;
+  ComputeClustering();
+
+  // 2 canonical views.
+  EXPECT_EQ(centers_.size(), 2);
+  EXPECT_EQ(centers_[0], kVertexIds[1]);
+  EXPECT_EQ(centers_[1], kVertexIds[2]);
+}
+
+// Increases similarity penalty so vertex 2 won't be chosen despite
+// it's view score.
+TEST_F(CanonicalViewsTest, SimilarityPenaltyTest) {
+  options_.min_views = 0;
+  options_.size_penalty_weight = 0.5;
+  options_.similarity_penalty_weight = 3.0;
+  options_.view_score_weight = 1.0;
+  ComputeClustering();
+
+  // 2 canonical views.
+  EXPECT_EQ(centers_.size(), 1);
+  EXPECT_EQ(centers_[0], kVertexIds[1]);
+}
+
+}  // namespace internal
+}  // namespace ceres