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unified norm computing; added generalized Hamming distance

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This commit is contained in:
Vadim Pisarevsky
2011-10-11 15:13:53 +00:00
parent c1277b6147
commit b74116e694
12 changed files with 485 additions and 589 deletions
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43
modules/core/src/matrix.cpp
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@@ -2161,43 +2161,6 @@ static void generateRandomCenter(const vector<Vec2f>& box, float* center, RNG& r
}
static inline float distance(const float* a, const float* b, int n)
{
int j = 0; float d = 0.f;
#if CV_SSE
if( USE_SSE2 )
{
float CV_DECL_ALIGNED(16) buf[4];
__m128 d0 = _mm_setzero_ps(), d1 = _mm_setzero_ps();
for( ; j <= n - 8; j += 8 )
{
__m128 t0 = _mm_sub_ps(_mm_loadu_ps(a + j), _mm_loadu_ps(b + j));
__m128 t1 = _mm_sub_ps(_mm_loadu_ps(a + j + 4), _mm_loadu_ps(b + j + 4));
d0 = _mm_add_ps(d0, _mm_mul_ps(t0, t0));
d1 = _mm_add_ps(d1, _mm_mul_ps(t1, t1));
}
_mm_store_ps(buf, _mm_add_ps(d0, d1));
d = buf[0] + buf[1] + buf[2] + buf[3];
}
else
#endif
{
for( ; j <= n - 4; j += 4 )
{
float t0 = a[j] - b[j], t1 = a[j+1] - b[j+1], t2 = a[j+2] - b[j+2], t3 = a[j+3] - b[j+3];
d += t0*t0 + t1*t1 + t2*t2 + t3*t3;
}
}
for( ; j < n; j++ )
{
float t = a[j] - b[j];
d += t*t;
}
return d;
}
/*
k-means center initialization using the following algorithm:
Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
@@ -2218,7 +2181,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
for( i = 0; i < N; i++ )
{
dist[i] = distance(data + step*i, data + step*centers[0], dims);
dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims);
sum0 += dist[i];
}
@@ -2236,7 +2199,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
int ci = i;
for( i = 0; i < N; i++ )
{
tdist2[i] = std::min(distance(data + step*i, data + step*ci, dims), dist[i]);
tdist2[i] = std::min(normL2Sqr_(data + step*i, data + step*ci, dims), dist[i]);
s += tdist2[i];
}
@@ -2434,7 +2397,7 @@ double cv::kmeans( InputArray _data, int K,
for( k = 0; k < K; k++ )
{
const float* center = centers.ptr<float>(k);
double dist = distance(sample, center, dims);
double dist = normL2Sqr_(sample, center, dims);
if( min_dist > dist )
{