From: Pierre-Emmanuel Viel <p.emmanuel.viel@gmail.com>
Date: Tue, 17 Dec 2013 12:04:49 +0000 (+0100)
Subject: Apply to KMeansIndex KMeanspp the same modification as in HierarchicalClusteringIndex
X-Git-Tag: submit/tizen_ivi/20141117.190038~2^2~198^2~7^2~60^2~2
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=5aeeaa6fce4016fd626f31f56025cf83ff07576a;p=profile%2Fivi%2Fopencv.git

Apply to KMeansIndex KMeanspp the same modification as in HierarchicalClusteringIndex
---

diff --git a/modules/flann/include/opencv2/flann/kmeans_index.h b/modules/flann/include/opencv2/flann/kmeans_index.h
index 3fea956..3bf1204 100644
--- a/modules/flann/include/opencv2/flann/kmeans_index.h
+++ b/modules/flann/include/opencv2/flann/kmeans_index.h
@@ -211,6 +211,7 @@ public:
 
         for (int i = 0; i < n; i++) {
             closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+            closestDistSq[i] *= closestDistSq[i];
             currentPot += closestDistSq[i];
         }
 
@@ -236,7 +237,10 @@ public:
 
                 // Compute the new potential
                 double newPot = 0;
-                for (int i = 0; i < n; i++) newPot += std::min( distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols), closestDistSq[i] );
+                for (int i = 0; i < n; i++) {
+                    DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+                    newPot += std::min( dist*dist, closestDistSq[i] );
+                }
 
                 // Store the best result
                 if ((bestNewPot < 0)||(newPot < bestNewPot)) {
@@ -248,7 +252,10 @@ public:
             // Add the appropriate center
             centers[centerCount] = indices[bestNewIndex];
             currentPot = bestNewPot;
-            for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols), closestDistSq[i] );
+            for (int i = 0; i < n; i++) {
+                DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
+                closestDistSq[i] = std::min( dist*dist, closestDistSq[i] );
+            }
         }
 
         centers_length = centerCount;