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）