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probably, ultimately fixed the problem of empty clusters in kmeans; added test for singular kmeans cases

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This commit is contained in:
Vadim Pisarevsky
2012-04-05 13:01:34 +00:00
parent d80651d99a
commit c6a27b3d2f
2 changed files with 63 additions and 3 deletions
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14
modules/core/src/matrix.cpp
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@@ -2489,7 +2489,7 @@ double cv::kmeans( InputArray _data, int K,
}
int* labels = _labels.ptr<int>();
Mat centers(K, dims, type), old_centers(K, dims, type);
Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
vector<int> counters(K);
vector<Vec2f> _box(dims);
Vec2f* box = &_box[0];
@@ -2533,7 +2533,7 @@ double cv::kmeans( InputArray _data, int K,
for( a = 0; a < attempts; a++ )
{
double max_center_shift = DBL_MAX;
for( iter = 0; iter < criteria.maxCount && max_center_shift > criteria.epsilon; iter++ )
for( iter = 0;; )
{
swap(centers, old_centers);
@@ -2609,7 +2609,11 @@ double cv::kmeans( InputArray _data, int K,
double max_dist = 0;
int farthest_i = -1;
float* new_center = centers.ptr<float>(k);
float* old_center = centers.ptr<float>(max_k);
float* _old_center = centers.ptr<float>(max_k);
float* old_center = temp.ptr<float>();
float scale = 1.f/counters[max_k];
for( j = 0; j < dims; j++ )
old_center[j] = _old_center[j]*scale;
for( i = 0; i < N; i++ )
{
@@ -2627,6 +2631,7 @@ double cv::kmeans( InputArray _data, int K,
counters[max_k]--;
counters[k]++;
labels[farthest_i] = k;
sample = data.ptr<float>(farthest_i);
for( j = 0; j < dims; j++ )
@@ -2658,6 +2663,9 @@ double cv::kmeans( InputArray _data, int K,
}
}
}
if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
break;
// assign labels
compactness = 0;