常用的特征级融合方法有哪些?并给出matlab代码
时间: 2023-11-26 12:06:30 浏览: 168
常用的特征级融合方法有:
1. 加权平均法:将多个特征提取器的输出进行加权平均,权重可以根据经验或者训练得到。
Matlab代码示例:
```matlab
function [fused_feature] = weighted_average(features, weights)
% features: cell array of feature matrices
% weights: vector of weights for each feature
% fused_feature: fused feature matrix
num_features = length(features);
fused_feature = zeros(size(features{1}));
for i = 1:num_features
fused_feature = fused_feature + weights(i) * features{i};
end
fused_feature = fused_feature / sum(weights);
end
```
2. 最大值法:将多个特征提取器的输出取最大值作为融合结果。
Matlab代码示例:
```matlab
function [fused_feature] = max_pooling(features)
% features: cell array of feature matrices
% fused_feature: fused feature matrix
num_features = length(features);
fused_feature = max(features{1}, [], 3);
for i = 2:num_features
fused_feature = max(fused_feature, features{i}, [], 3);
end
end
```
3. 最小值法:将多个特征提取器的输出取最小值作为融合结果。
Matlab代码示例:
```matlab
function [fused_feature] = min_pooling(features)
% features: cell array of feature matrices
% fused_feature: fused feature matrix
num_features = length(features);
fused_feature = min(features{1}, [], 3);
for i = 2:num_features
fused_feature = min(fused_feature, features{i}, [], 3);
end
end
```
4. 加和法:将多个特征提取器的输出直接相加作为融合结果。
Matlab代码示例:
```matlab
function [fused_feature] = sum_pooling(features)
% features: cell array of feature matrices
% fused_feature: fused feature matrix
num_features = length(features);
fused_feature = zeros(size(features{1}));
for i = 1:num_features
fused_feature = fused_feature + features{i};
end
end
```
5. PCA融合法:将多个特征提取器的输出进行主成分分析(PCA)降维,然后再进行加权平均。
Matlab代码示例:
```matlab
function [fused_feature] = pca_fusion(features, weights)
% features: cell array of feature matrices
% weights: vector of weights for each feature
% fused_feature: fused feature matrix
num_features = length(features);
num_samples = size(features{1}, 2);
num_dims = size(features{1}, 1);
data = zeros(num_samples, num_features * num_dims);
for i = 1:num_features
data(:, (i-1)*num_dims+1:i*num_dims) = features{i}';
end
coeff = pca(data);
fused_feature = zeros(num_dims, num_samples);
for i = 1:num_features
feature_pca = coeff((i-1)*num_dims+1:i*num_dims, 1:num_dims) * features{i};
fused_feature = fused_feature + weights(i) * feature_pca;
end
fused_feature = fused_feature / sum(weights);
end
```