实现了最简单的框架

Data_Analyse/CalculateMAP.asv

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+%Calculate the MAP value under certain truncated level T
+function value = CalculateMAP(rank,data,T)
+num_rel = 0;
+num_img = size(rank,1);
+map = 0;
+count = 0;
+T = min(T,num_img);
+for i = 1:T
+    seq = rank(i,1);
+    rel = data(seq,3);
+    count = count + 1;
+    num_rel = num_rel + rel;
+    if rel == 1
+        map = map + num_rel/count;
+    end
+end
+value = map / num_rel;

Data_Analyse/CalculateMAP.m

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+%Calculate the MAP value under certain truncated level T
+function value = CalculateMAP(rank,data,T)
+num_rel = 0;
+num_img = size(rank,1);
+map = 0;
+count = 0;
+T = min(T,num_img);
+for i = 1:T
+    seq = rank(i,1);
+    rel = data(seq,3);
+    count = count + 1;
+    num_rel = num_rel + rel;
+    if rel == 1
+        map = map + num_rel/count;
+    end
+end
+value = map / num_rel;

Data_Analyse/GData.asv

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+%Analyze the data
+%function GData
+label_dir = 'G:/LINBIN_DATA/labels.txt';
+Table = load(label_dir);
+query_set = unique(Table(:,1));
+for i = 1:length(query_set)
+    query = query_set(i);
+    %Extract the given query from the table
+    corres = find(Table(:,1) == query);
+    query_part = Table(corres(1):corres(length(corres)));
+end
+

Data_Analyse/GData.m

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+%Analyze the data
+%function GData
+function rec_query = GData(query)
+label_dir = 'G:/LINBIN_DATA/labels.txt';
+Table = load(label_dir);
+%Extract the given query from the table
+corres = find(Table(:,1) == query);
+query_part = Table(corres(1):corres(length(corres)),:);
+%order the set according to the number of the image
+rec_query = sortrows(query_part,2);
+

Similarity/CalculateSim.m

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+%Calculate the similarity value between two features
+function value = CalculateSim(fea1,fea2)
+%Calculate the distance between two features
+dis =(norm(fea1-fea2))^2;
+value = 1/(dis+0.5);

Similarity/Fea2Sim.m

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+%Turning Feature Matrix into Similarity Matrix
+function sim = Fea2Sim(fea)
+num_img = size(fea,1);
+dim = size(fea,2);
+sim = zeros(num_img,num_img);
+for i = 1:num_img
+    fea1 = fea(i,:);
+    for j = i+1 : nu_img
+        fea2 = fea(j,:);
+        simvalue = CalculateSim(fea1,fea2);
+        sim(i,j) = simvalue;
+        sim(j,i) = simvalue;
+    end
+end
+
+

Visual Rank/QDE_Estimation.asv

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+%The procedure of query difficulty estimation using coherence score
+function cos_score = QDE_Estimation(sim_mat, T)
+%%
+%%%Calculate the Trsim
+%simpath = 'G:\LINBIN_DATA\simmat';
+%v = SimThresh(simpath);
+thresh = 0.6294;
+%%
+num_img = size(sim_mat,1);
+if T > num_img
+    cos_score = -1;
+else
+    for i = 1:
+
+
+
+
+
+
+
+
+function value = SimThresh(sim_path)
+%load the similarity value of the dataset
+%For web353, the value equals to 0.6294
+fea_dir = dir(sim_path);
+sim_pairs = [];
+for i = 1:length(fea_dir)
+    fea_name = fea_dir(i).name;
+    if(strcmp(fea_name,'.') == true || strcmp(fea_name, '..')==true)
+        continue;
+    end
+    fea_path = fullfile(sim_path,fea_name);
+    load(fea_path);
+    num_img = size(sim_mat,1);
+    for ii = 1:num_img
+        for jj = ii+1:num_img
+            sim_pairs = [sim_pairs; sim_mat(ii,jj)];
+        end
+    end
+end
+[Y,I] = sort(sim_pairs,1,'ascend');
+num_pairs = length(sim_pairs);
+value_index = floor(num_pairs*0.8);
+value = sim_pairs(value_index);

Visual Rank/QDE_Estimation.m

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+%The procedure of query difficulty estimation using coherence score
+function cos_score = QDE_Estimation(sim_mat, T)
+%%
+%%%Calculate the Trsim
+%simpath = 'G:\LINBIN_DATA\simmat';
+%v = SimThresh(simpath);
+thresh = 0.6294;
+%%
+num_img = size(sim_mat,1);
+if T > num_img
+    cos_score = -1;
+else
+    cos_score = 0;
+    for i = 1:T
+        for j = 1:T
+            sim = sim_mat(i,j);
+            if sim > thresh
+                cos_score = cos_score + 1;
+            end
+        end
+    end
+    cos_score = cos_score / (T*(T-1));
+
+end
+
+
+
+
+
+
+function value = SimThresh(sim_path)
+%load the similarity value of the dataset
+%For web353, the value equals to 0.6294
+fea_dir = dir(sim_path);
+sim_pairs = [];
+for i = 1:length(fea_dir)
+    fea_name = fea_dir(i).name;
+    if(strcmp(fea_name,'.') == true || strcmp(fea_name, '..')==true)
+        continue;
+    end
+    fea_path = fullfile(sim_path,fea_name);
+    load(fea_path);
+    num_img = size(sim_mat,1);
+    for ii = 1:num_img
+        for jj = ii+1:num_img
+            sim_pairs = [sim_pairs; sim_mat(ii,jj)];
+        end
+    end
+end
+[Y,I] = sort(sim_pairs,1,'ascend');
+num_pairs = length(sim_pairs);
+value_index = floor(num_pairs*0.8);
+value = sim_pairs(value_index);

Visual Rank/VisualRank.asv

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+function rank = VisualRank(sim,d,rel)
+%Applying VisualRank Algorithm
+num = size(sim,1);
+rank = ones(num,1);
+iter_time = 100;
+%column normalize the matrix
+for i = 1:num
+    fea = sim(:,i);
+    fea = fea./sum(fea);
+    sim(:,i) = fea;
+end
+%parameters settings
+p = zeros(num,1);
+for i = 1:rel
+    p(i) = 1/rel;
+end
+i = 0;
+while i < iter_time
+    rank = d*sim*rank + (1-d)*p;
+end
+
+
+

Visual Rank/VisualRank.m

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+function rank = VisualRank(sim,d,rel)
+num = size(sim,1);
+rank = ones(num,1);
+iter_time = 100;
+%column normalize the matrix
+for i = 1:num
+    fea = sim(:,i);
+    fea = fea./sum(fea);
+    sim(:,i) = fea;
+end
+%parameters settings
+p = zeros(num,1);
+%In case when num is smaller than rel
+rnum = min(rel,num);
+for i = 1:rnum
+    p(i) = 1/rnum;
+end
+i = 0;
+while i < iter_time
+    rank = d*sim*rank + (1-d)*p;
+    i = i+ 1;
+end
+
+
+

main.m

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+%The main function
+addpath('Visual Rank');
+addpath('Feature');
+addpath('Similarity');
+addpath('Data_Analyse');
+img_path = 'G:\LINBIN_DATA\DCNN_O';
+sim_path = 'G:\LINBIN_DATA\simmat';
+%%
+%load the similarity matrix
+sim_dir = dir(sim_path);
+sum_ap0 = 0;
+sum_ap1 = 0;
+for i = 1:length(sim_dir)
+    file_name = sim_dir(i).name;
+	if(strcmp(file_name,'.') == false && strcmp(file_name,'..')==false)
+        sim_file = fullfile(sim_path,file_name);
+        %%
+        %Get the index of the query
+        index1 = strfind(file_name,'_');
+        index2 = strfind(file_name,'.');
+        query = str2num(file_name(index1+1:index2-1))
+        %load the GroundTruth Data
+        gdata = GData(query);
+        load(sim_file);
+        %%
+        %1. Don't re-rank the images
+        num_img = size(sim_mat,1);
+        gindex = zeros(num_img,1);
+        for ii = 1:num_img
+            gindex(ii) = ii;
+        end
+        %2.Using Visual_Rank to re-rank the images
+        %Baseline method        
+        rankscore = VisualRank(sim_mat,0.85,30);        
+        [rank,index] = sort(rankscore,'descend');
+        %%
+        %Caluclate the MAP
+        AP0 = CalculateMAP(gindex,gdata,num_img);
+        AP1 = CalculateMAP(index,gdata,num_img);
+        sum_ap0 = AP0 + sum_ap0;
+        sum_ap1 = AP1 + sum_ap1;
+    end
+end
+
+sum_ap0 = sum_ap0 / 353
+sum_ap1 = sum_ap1 / 353
+
+