Caffe 提供了matlab接口,可以用于提取图像的feature.

首先，打开终端，进入caffe的主目录下,然后打开Matlab
...

默认的文件是：classification_demo.m，
里面有两个函数。把路径设置完了之后，就可以试试运行了。我把它改成了我比较习惯的方式，即:xiao.m 

clc;

close all;

clear all;

num1=1000; % 提取多少张图像的feature ？

use_gpu=1; % 运行的模式，gpu or cpu ?

if exist(\'../+caffe\', \'dir\')

addpath(\'..\');

else

error(\'Please run this demo from caffe/matlab/demo\');

end

if exist(\'use_gpu\', \'var\') && use_gpu

caffe.set_mode_gpu();

gpu_id = 0; % we will use the first gpu in this demo

caffe.set_device(gpu_id);

else

caffe.set_mode_cpu();

end

model_dir = \'../../models/bvlc_reference_caffenet/\';

net_model = [model_dir \'deploy.prototxt\'];

net_weights = [model_dir \'bvlc_reference_caffenet.caffemodel\'];

phase = \'train\';

if ~exist(net_weights, \'file\')

error(\'Please download CaffeNet from Model Zoo before you run this demo\');

end

% Initialize a network

net = caffe.Net(net_model, net_weights, phase);

fprintf(\'Read PETA-dataset images and Extract features ...\n\');

dataPath = \'../../Link to PETA dataset/3DPeS/archive/\';

images = dir([dataPath,\'*.bmp\']);

for num=1:num1

% disp(\'read the\', num2str(num) ,\'th\', \'/\', num2str(num1) ,\'image, please waiting ...\');

im = imread([dataPath, images(num).name]);

fid1 = fopen(\'train_feature_id.txt\', \'a\');

fprintf(fid1, \'%s \n\', images(num).name );

fclose(fid1);

% prepare oversampled input

% input_data is Height x Width x Channel x Num

% tic;

input_data = {prepare_image(im)};

% toc;

% do forward pass to get scores ­

% scores are now Channels x Num, where Channels == 1000

% tic;

% The net forward function. It takes in a cell array of N-D arrays

% (where N == 4 here) containing data of input blob(s) and outputs a cell

% array containing data from output blob(s)

scores = net.forward(input_data);

Score = scores{1,1};

fid = fopen(\'train_feature.txt\', \'a\');

fprintf(fid, \'%f \n\', Score);

fclose(fid);

% toc;

% scores = scores{1};

% scores = mean(scores, 2); % take average scores over 10 crops

% [~, maxlabel] = max(scores);

% call caffe.reset_all() to reset caffe

% caffe.reset_all();

end

这个函数调用了 prepare_image.m，因为caffe对图像的处理方式和常规的方式不同，需要对读取的图像做一个转换工作，而这个过程是由 prepare_image.m来完成的。

运行之后，就会生成两个txt文件，一个是读取图像的图像名字，另一个是对应所提取的feature。

        (a) train_feature_id          (b)  train_feature

由于这个网络的输入是255×255的图像，会对其做一个crop，生成10个227×227的图像，所以最后的feature为4096×10
的矩阵，而不是4096的向量。。。