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avoid empty clusters in k-means in a more elegant way (relates to ticket #7698)

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This commit is contained in:
Vadim Pisarevsky 2012-03-28 14:32:23 +00:00
parent 806aab164b
commit d62e486b69
1 changed files with 52 additions and 8 deletions
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60
modules/core/src/matrix.cpp
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@ -2434,6 +2434,7 @@ double cv::kmeans( InputArray _data, int K,
attempts = std::max(attempts, 1);
CV_Assert( data.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( N >= K );
_bestLabels.create(N, 1, CV_32S, -1, true);
@ -2557,18 +2558,61 @@ double cv::kmeans( InputArray _data, int K,
if( iter > 0 )
max_center_shift = 0;
for( k = 0; k < K; k++ )
{
if( counters[k] != 0 )
continue;
// if some cluster appeared to be empty then:
// 1. find the biggest cluster
// 2. find the farthest from the center point in the biggest cluster
// 3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
int max_k = 0;
for( int k1 = 1; k1 < K; k++ )
{
if( counters[max_k] < counters[k1] )
max_k = k1;
}
double max_dist = 0;
int farthest_i = -1;
float* new_center = centers.ptr<float>(k);
float* old_center = centers.ptr<float>(max_k);
for( i = 0; i < N; i++ )
{
if( labels[i] != max_k )
continue;
sample = data.ptr<float>(i);
double dist = normL2Sqr_(sample, old_center, dims);
if( max_dist <= dist )
{
max_dist = dist;
farthest_i = i;
}
}
counters[max_k]--;
counters[k]++;
sample = data.ptr<float>(farthest_i);
for( j = 0; j < dims; j++ )
{
old_center[j] -= sample[j];
new_center[j] += sample[j];
}
}
for( k = 0; k < K; k++ )
{
float* center = centers.ptr<float>(k);
if( counters[k] != 0 )
{
float scale = 1.f/counters[k];
for( j = 0; j < dims; j++ )
center[j] *= scale;
}
else
generateRandomCenter(_box, center, rng);
CV_Assert( counters[k] != 0 );
float scale = 1.f/counters[k];
for( j = 0; j < dims; j++ )
center[j] *= scale;
if( iter > 0 )
{