Traditional Saliency Reloaded: A Good Old Model in New Shape

发在CVPR2015上的一篇文章，读起来并不是很困难，思路也比较纯粹。作者也开源了代码，在这里下载：http://pages.iai.uni-bonn.de/frintrop_simone/vocus2.html问题是，作者的代码依赖于Boost C++ Library,但是我对这个库并不是很熟悉，配置起来可能不是那么方便。所以我根据自己的理解，用Matlab重写了一下，看起来效果还可以，希望能对需要的人有所帮助。需要指出的是，对于一些细节的理解可能不准确，比如说八度的个数，每个八度的层数，生成金字塔特征的过程等。因此，该代码只做测试用，未与原始C++版本代码对照修正，如果需要与论文方法做比较，请依据作者原始代码进行测评。

GetFeatureChannels.m

function [Intensity RG BY] = GetFeatureChannels( Img )if(length(size(Img))~=3)    disp('Input image must be a color image with RGB 3 channels!');    return;endR=Img(:,:,1);G=Img(:,:,2);B=Img(:,:,3);Intensity=(R+G+B)/3;RG=R-G;BY=B-(R+G)/2;end

 BuildMultiScaleTwinPyr.m

function [center_pyramid surround_pyramid] = BuildMultiScaleTwinPyr( input_image_channel,octave_num,layer_num,sig_center,sig_surround )% 第几个八度(o)和八度中的第几层(s)两个变量构成了高斯金字塔的尺度空间。% 一个八度中图像的长和宽是相等的，即变量o控制的是塔中尺寸这个尺度;% s控制的是一个八度中不同的模糊程度。center_pyramid=cell(octave_num,layer_num);surround_pyramid=cell(octave_num,layer_num);tmpimg=input_image_channel;for o=0:octave_num-1    for s=0:layer_num-1        sig_total=power(2.0,double(o)+double(s)/double(layer_num-1))*sig_center;        gaussFilter=fspecial('gaussian',[5 5],sig_total);        center_pyramid{o+1,s+1}=double(imfilter(tmpimg,gaussFilter,'replicate'));        sig_ratio=sqrt(sig_surround*sig_surround-sig_center*sig_center);        ratioFilter=fspecial('gaussian',[5 5],sig_ratio);        surround_pyramid{o+1,s+1}=double(imfilter(center_pyramid{o+1,s+1},ratioFilter,'replicate'));    end    tmpimg=imresize(tmpimg,[size(tmpimg,1)/2 size(tmpimg,2)/2]);endend

FeatureFusion.m

function FeatureMap = FeatureFusion( ContrastPyr )%ContrastPyr should be in CELL format. Each row represents an OCTAVE with%multiple layers (in our setup, we set 3 layers in one octave). FeatureMap%should be the same size as elements in first row of ContrastPyr, that is,%FeatureMap is the same size as original input image. Consequently, before%fusing ContrastPyr, we should unify(enlarge) contrast maps to the same%size, i.e., size of original input image.[height width]=size(ContrastPyr{1,1}); %standard sizeFeatureMap=zeros(height,width);[m n]=size(ContrastPyr);SameSizeMap=cell(m,n);for i=1:m % Remain first row of ContrastPyr unchanged    for j=1:n        if(i==1)            SameSizeMap{i,j}=ContrastPyr{i,j};        else            SameSizeMap{i,j}=imresize(ContrastPyr{i,j},[height width],'bicubic');        end    endendfor i=1:m    for j=1:n        FeatureMap=FeatureMap+SameSizeMap{i,j};    endendend

Normalize.m

function OutImg = Normalize(InImg)  ymax=255;ymin=0;  xmax = max(max(InImg)); %求得InImg中的最大值  xmin = min(min(InImg)); %求得InImg中的最小值  OutImg = round((ymax-ymin)*(InImg-xmin)/(xmax-xmin) + ymin); %归一化并取整  end  

Main.m

clc;clear;% Path settinginputImgPath = 'Imgs';                 % input image pathresSalPath = 'SAL_MAP';                     % result pathif ~exist(resSalPath, 'file')    mkdir(resSalPath);endaddpath(genpath(inputImgPath));% Parameter settingoctave_num=5; %5个八度layer_num=3; %每个八度下3层sig_center=3;sig_surround=13;%% Create twin pyramidsimgFiles = dir(inputImgPath);for indImg = 1:2:length(imgFiles)-2        % read image    imgPath = fullfile(inputImgPath, imgFiles(indImg+2).name);    img= imread(imgPath);     [Intensity RG BY]=GetFeatureChannels(img);    % Build twin pyramids for Internsity    [InCenterPyr InSurroundPyr] = BuildMultiScaleTwinPyr(Intensity,octave_num,layer_num,sig_center,sig_surround);    % Build twin pyramids for RG    [RGCenterPyr RGSurroundPyr] = BuildMultiScaleTwinPyr(RG,octave_num,layer_num,sig_center,sig_surround);    % Build twin pyramids for BY    [BYCenterPyr BYSurroundPyr] = BuildMultiScaleTwinPyr(BY,octave_num,layer_num,sig_center,sig_surround);        %% Create contrast pyramids    [m n]=size(InCenterPyr);    InOnOff=cell(m,n);    InOffOn=cell(m,n);    RGOnOff=cell(m,n);    RGOffOn=cell(m,n);    BYOnOff=cell(m,n);    BYOffOn=cell(m,n);    for i=1:m        for j=1:n            InOnOff{i,j}=InCenterPyr{i,j}-InSurroundPyr{i,j};            tmp=InOnOff{i,j};            InOnOff{i,j}(find(tmp(:)<0))=0;            InOffOn{i,j}=InSurroundPyr{i,j}-InCenterPyr{i,j};            tmp=InOffOn{i,j};            InOffOn{i,j}(find(tmp(:)<0))=0;            RGOnOff{i,j}=RGCenterPyr{i,j}-RGSurroundPyr{i,j};            tmp=RGOnOff{i,j};            RGOnOff{i,j}(find(tmp(:)<0))=0;            RGOffOn{i,j}=RGSurroundPyr{i,j}-RGCenterPyr{i,j};            tmp=RGOffOn{i,j};            RGOffOn{i,j}(find(tmp(:)<0))=0;            BYOnOff{i,j}=BYCenterPyr{i,j}-BYSurroundPyr{i,j};            tmp=BYOnOff{i,j};            BYOnOff{i,j}(find(tmp(:)<0))=0;            BYOffOn{i,j}=BYSurroundPyr{i,j}-BYCenterPyr{i,j};            tmp=BYOffOn{i,j};            BYOffOn{i,j}(find(tmp(:)<0))=0;        end    end        %% FeatureMaps    InOnOffFusion=FeatureFusion(InOnOff);    InOffOnFusion=FeatureFusion(InOffOn);    RGOnOffFusion=FeatureFusion(RGOnOff);    RGOffOnFusion=FeatureFusion(RGOffOn);    BYOnOffFusion=FeatureFusion(BYOnOff);    BYOffOnFusion=FeatureFusion(BYOffOn);    %% FeatureMaps Fusion    IntensityFeature=Normalize((InOnOffFusion+InOffOnFusion)/2);    RGFeature=Normalize((RGOnOffFusion+RGOffOnFusion)/2);    BYFeature=Normalize((BYOnOffFusion+BYOffOnFusion)/2);    %% Generate Saliency Map    FeatureMap=Normalize((IntensityFeature+RGFeature+BYFeature)/3);    figure(indImg);    subplot(121);    imshow(img,[]);    title('Original Image');    subplot(122);    imshow(FeatureMap,[]);    title('Saliency Map');       pause(2);    close(figure(indImg));end     

再次提醒，该代码可能不能准确反映论文原始意图，仅做测试使用，顺便可能对需要的人有所启发。贴几张效果图

有问题可联系 jzwangATbjtuDOTeduDOTcn讨论交流。