图像检索：RGBHistogram+欧几里得距离|卡方距离

RGBHistogram：

分别计算把彩色图像的三个通道R、G、B的一维直方图，然后把这三个通道的颜色直方图结合起来，就是颜色的描述子RGBHistogram。

下面给出计算RGBHistogram的代码：

<span style="font-family:Microsoft YaHei;font-size:18px;">#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

const int HISTSIZE = 8;
int main( int, char** argv )
{
  Mat src, dst;

  /// Load image
  src = imread( argv[1], 1 );

  if( !src.data || (src.channels() !=3))
    { return -1; }

  Mat rgbFeature = bgrHistogram(src);
 
  return 0;
}

Mat bgrHistogram(const Mat& src)
{
	//分离B、G、R通道
	vector<Mat> bgr_planes;
	split(src,bgr_planes);


  float range[] = { 0, 256 } ;
  const float* histRange = { range };

  bool uniform = true; bool accumulate = false;

  Mat hist1d,normHist1d,hist;

  for(int i = 0 ;i < 3;i++)
  {
	  calcHist( &bgr_planes[i], 1, 0, Mat(), hist1d, 1, &HISTSIZE, &histRange, uniform, accumulate );
	  normalize(hist1d,hist1d,1.0,0.0,CV_L1);
	  hist.push_back(hist1d);
  }
  return hist;
}
</span>

第二步：颜色描述子已经计算出，选取什么样的距离。

对于距离我们先选取两种：

第一种：欧几里得距离

#include<iostream>
#include <fstream>
#include <stdio.h>
using namespace std;

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;

const int HISTSIZE = 16;
Mat bgrHistogram(const Mat& src);
double  euclideanDistance(const Mat & src1,const Mat &src2);
int main( int, char** argv )
{
  //定义文件流，只能读取
	ifstream inPutFile(argv[1],ios::in);
	if(! inPutFile)
	{
		cerr << "File Open Erro !"<<endl;
		return -1;
	}

	//读取文件流中的每一行，并赋值给fileName,形成查询数据库
	string fileName ;
	Mat image,histogram,sourceHisrogram;
	vector<Mat> histograms;

	map<int,string>index;//图像的索引
	index.clear();
	int  number = 0;
	histograms.clear();
	while(getline(inPutFile,fileName))
	{
		index.insert(pair<int,string>(number,fileName));
		number++;
		image = imread(fileName,1);
		histogram = bgrHistogram(image);
		histograms.push_back(histogram);
	}
	//待搜索的图像
	number = 0;
	Mat imageSource = imread(argv[2],1);
	sourceHisrogram = bgrHistogram(imageSource);
	vector<Mat>::iterator iter;
	map<double,int>distance;
	for(iter = histograms.begin();iter != histograms.end();iter++)
	{
		distance.insert(pair<double,int>(euclideanDistance(sourceHisrogram,*iter),number));
		number++;
	}
	//显示距离最小的前五名的检索图像
	number = 0;
	map<double,int>::iterator mapiter;
	for(mapiter = distance.begin();mapiter != distance.end() && number <2;mapiter++,number++)
	{
		string simage = index.find((*mapiter).second) ->second;
		image = imread(simage,1);
		namedWindow(simage,1);
		imshow(simage,image);
	}
	waitKey(0);
}

Mat bgrHistogram(const Mat& src)
{
	//分离B、G、R通道
	vector<Mat> bgr_planes;
	split(src,bgr_planes);


  float range[] = { 0, 256 } ;
  const float* histRange = { range };

  bool uniform = true; bool accumulate = false;

  Mat hist1d,normHist1d,hist;

  for(int i = 0 ;i < 3;i++)
  {
	  calcHist( &bgr_planes[i], 1, 0, Mat(), hist1d, 1, &HISTSIZE, &histRange, uniform, accumulate );
	  normalize(hist1d,hist1d,1.0,0.0,CV_L1);
	  hist.push_back(hist1d);
  }
  return hist;
}

double  euclideanDistance(const Mat & src1,const Mat &src2)
{
	Mat pow2;
	pow(src1-src2,2.0,pow2);
	return 	sqrt(sum(pow2)[0]);
}

搜索数据库

运行结果：

第二种：卡方距离

#include<iostream>
#include <fstream>
#include <stdio.h>
using namespace std;

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;

const int HISTSIZE = 16;
Mat bgrHistogram(const Mat& src);
int main( int, char** argv )
{
  //定义文件流，只能读取
	ifstream inPutFile(argv[1],ios::in);
	if(! inPutFile)
	{
		cerr << "File Open Erro !"<<endl;
		return -1;
	}

	//读取文件流中的每一行，并赋值给fileName,形成查询数据库
	string fileName ;
	Mat image,histogram,sourceHisrogram;
	vector<Mat> histograms;

	map<int,string>index;//图像的索引
	index.clear();
	int  number = 0;
	histograms.clear();
	while(getline(inPutFile,fileName))
	{
		index.insert(pair<int,string>(number,fileName));
		number++;
		image = imread(fileName,1);
		histogram = bgrHistogram(image);
		histograms.push_back(histogram);
	}
	//待搜索的图像
	number = 0;
	Mat imageSource = imread(argv[2],1);
	sourceHisrogram = bgrHistogram(imageSource);
	vector<Mat>::iterator iter;
	map<double,int>distance;
	for(iter = histograms.begin();iter != histograms.end();iter++)
	{
		distance.insert(pair<double,int>(compareHist(sourceHisrogram,*iter,CV_COMP_CHISQR),number));
		number++;
	}
	//显示距离最小的前五名的检索图像
	number = 0;
	map<double,int>::iterator mapiter;
	for(mapiter = distance.begin();mapiter != distance.end() && number <2;mapiter++,number++)
	{
		string simage = index.find((*mapiter).second) ->second;
		image = imread(simage,1);
		namedWindow(simage,1);
		imshow(simage,image);
	}
	waitKey(0);
}

Mat bgrHistogram(const Mat& src)
{
	//分离B、G、R通道
	vector<Mat> bgr_planes;
	split(src,bgr_planes);


  float range[] = { 0, 256 } ;
  const float* histRange = { range };

  bool uniform = true; bool accumulate = false;

  Mat hist1d,normHist1d,hist;

  for(int i = 0 ;i < 3;i++)
  {
	  calcHist( &bgr_planes[i], 1, 0, Mat(), hist1d, 1, &HISTSIZE, &histRange, uniform, accumulate );
	  normalize(hist1d,hist1d,1.0,0.0,CV_L1);
	  hist.push_back(hist1d);
  }
  return hist;
}

搜索图片数据库

运行结果：（我只提取前两副距离最近的图片）