- Visual C++数字图像模式识别典型案例详解
- 冯伟兴 梁洪 王臣业编著
- 697字
- 2025-03-16 03:50:43
4.4.3 模糊聚类
编程实现
[1] 在类CCluster中添加函数FuzzyCluster()，进行有关图像的模糊聚类。具体实现代码如代码4-4所示。
代码4-4 FuzzyCluster()函数
void CCluster::FuzzyCluster()
{
GetFeature(); //获得所有样品特征
double ＊dis, ＊tempdis;
double dismax;
int i,j;
int distype;
dismax=0;
dis=new double [patternnum＊patternnum]; //模糊系数矩阵
tempdis=new double [patternnum＊patternnum];
return;
distype=julizl; //此处为声明模糊距离的总类
//得到初始模糊距离
for ( i=0; i<patternnum; i++)
for (j=0; j<patternnum; j++)
{
dis[i＊patternnum+j]=GetFuzzyDistance(m_pattern[i],m_pattern[j],
distype);//distype
}
//构造等价类
bool flag;
flag=true;
while (flag)
{
flag=false;
for ( i=0; i<patternnum; i++)
for ( j=0; j<patternnum; j++)
if (i==j)//对角线为1
tempdis[i＊patternnum+j]=1;
else
tempdis[i＊patternnum+j]=GetDistance(dis,i,j);
for ( i=0; i<patternnum; i++)
{
for ( j=0; j<patternnum; j++)
if ((tempdis[i＊patternnum+j]-dis[i＊patternnum+j])＊(tempdis
[i＊patternnum+j]-dis[i＊patternnum+j])>0.000001)//(tdis[i][j]!=dis[i][j])
{
flag=true;
break;
}
if (flag)
break;
}
for ( i=0; i<patternnum＊patternnum; i++)
dis[i]=tempdis[i];
}
//输出模糊矩阵
CString ts,sout;
//第一行标号1,2, …, patternnum
sout.Insert(sout.GetLength()," \t");
for (i=1; i<=patternnum; i++)
{
ts.Format("%d \t",i);
sout.Insert(sout.GetLength(),ts);
}
sout.Insert(sout.GetLength(),"\n\n");
for (i=0; i<patternnum; i++)
{
//每列开头标号
ts.Format("%d \t",i+1);
sout.Insert(sout.GetLength(),ts);
//输出模糊系数矩阵
for(j=0; j<patternnum; j++)
{
ts.Format("%0.3f\t",dis[i＊patternnum+j]);
sout.Insert(sout.GetLength(),ts);
if ((j+1)%patternnum==0)
sout.Insert(sout.GetLength(),"\n\n\n");
}
}
MessageBox(NULL, sout, "模糊矩阵", MB_OK);
delete []tempdis;
double ＊xishu=new double [patternnum＊patternnum];
for(i=0; i<patternnum＊patternnum; i++)
xishu[i]=-1;
int pointer=0;
//记录模糊系数矩阵中不同的系数
for ( i=0; i<patternnum; i++)
for ( j=i; j<patternnum; j++)
{
bool done=false;
for (int k=0; k<pointer; k++)
{
if ((xishu[k]-dis[i＊patternnum+j])＊(xishu[k]-dis[i＊patternnum+j])<0.000001)
{
done=true;
break;
}
}
if (!done)
{
xishu[pointer]=dis[i＊patternnum+j];
pointer++;
}
}
for(i=0; i<pointer-1; i++)//对阈值由小到大排序
for (j=0; j<pointer-i-1; j++)
{
if (xishu[j]>xishu[j+1])
{
double temp=xishu[j];
xishu[j]=xishu[j+1];
xishu[j+1]=temp;
}
}
CString s,str;
for (i=0; i<pointer; i++)
{
s.Format("%0.4f ",xishu[i]);
str.Insert(str.GetLength(),s);
}
delete []xishu;
//用户输入聚类阈值
double yz;
DLGmohu1 mohu;
mohu.m_Mohuxishu=str;
if (mohu.DoModal()==IDOK)
{
yz=mohu.m_Mohuyuzhi;
}
else return;
int ＊result;
//根据阈值输出聚类结果
result=new int [patternnum＊patternnum];
for (i=0; i<patternnum＊patternnum; i++)
if (dis[i]>=yz)
result[i]=1;
else
result[i]=0;
//分类后输出
//第一行标号
sout="";
sout.Insert(-1, "每行中\"1\"对应的列为同一类");
sout.Insert(sout.GetLength(),"\n\n");
sout.Insert(sout.GetLength()," \t");
for (i=1; i<=patternnum; i++)
{
ts.Format("%d \t",i);
sout.Insert(sout.GetLength(),ts);
}
sout.Insert(sout.GetLength(),"\n\n");
for (i=0; i<patternnum; i++)
{
//每列开头标号
ts.Format("%d \t",i+1);
sout.Insert(sout.GetLength(),ts);
for(j=0; j<patternnum; j++)//首行为原模糊系数
{
ts.Format("%0.3f\t",dis[i＊patternnum+j]);
sout.Insert(sout.GetLength(),ts);
if ((j+1)%patternnum==0)
sout.Insert(sout.GetLength(),"\n");
}
sout.Insert(sout.GetLength()," \t");
for(j=0; j<patternnum; j++)//次行为根据阈值修改后的系数“1”或“0”
{
ts.Format("%d\t",result[i＊patternnum+j]);
sout.Insert(sout.GetLength(),ts);
if ((j+1)%patternnum==0)
sout.Insert(sout.GetLength(),"\n\n");
}
}
MessageBox(NULL, sout, "分类前后的矩阵对照", MB_OK);
centernum=0;
for (i=0; i<patternnum; i++)//按照阈值分类
for (j=i; j<patternnum; j++)
{
if (result[i＊patternnum+j]==1)
{
if (m_pattern[i].category!=0)
m_pattern[j].category=m_pattern[i].category;
else if (m_pattern[j].category!=0)
m_pattern[i].category=m_pattern[j].category;
else
{
centernum++;
m_pattern[j].category=centernum;
m_pattern[i].category=centernum;
}
}
}
delete []dis;
delete []result;
}
void CCluster::huasz(int p,int q,int t)
{
int tmp;tmp=p; p=q;q=tmp;///////////////////////////////////////////////
int i,j;
for(i=0;i<16;i++)
for(j=0;j<16;j++)
＊(m_pData+(height-(i+p))＊wide+j+q)=sz[t][(15-i)＊16+j]＊255;
}
[2] 上述代码中，GetDistance ()函数用于返回模糊距离数组中第i行第j列的模糊积，该函数的实现代码如代码4-5所示。
代码4-5 GetDistance ()函数
double CCluster::GetDistance(double ＊dis, int i, int j)
{
double result;
result=0;
for(int t=0;t<patternnum;t++)
{
double td=dis[i＊patternnum+t]<dis[t＊patternnum+j]? dis[i＊patternnum+t]:dis[t＊patternnum+j];
if (result<td)
result=td;
}
return result;
}