svmtrain 中的错误，“Y 和 TRAINING 必须具有相同的行数”答案

【问题标题】：Error in svmtrain, "Y and TRAINING must have the same number of rows"svmtrain 中的错误，“Y 和 TRAINING 必须具有相同的行数”
【发布时间】：2015-04-03 13:24:00
【问题描述】：

我正在使用 svmtrain 函数对图像进行分类。我遇到了这样的错误。

Error using svmtrain (line 253)
Y and TRAINING must have the same number of rows.

Error in svm5 (line 80)
SVMStruct = svmtrain(Training_Set , train_label, 'kernel_function', 'linear');

Training_Set 包含图像集，而 train_lable 是用于识别输入图像的类。参考完整代码

clc
clear all

% Load Datasets

Dataset = 'D:\majorproject\image\traindata\';   
Testset  = 'D:\majorproject\image\testset\';


% we need to process the images first.
% Convert your images into grayscale
% Resize the images

width=100; height=100;
DataSet      = cell([], 1);

 for i=1:length(dir(fullfile(Dataset,'*.jpg')))

     % Training set process
     k = dir(fullfile(Dataset,'*.jpg'));
     k = {k(~[k.isdir]).name};
     for j=1:length(k)
        tempImage       = imread(horzcat(Dataset,filesep,k{j}));
        imgInfo         = imfinfo(horzcat(Dataset,filesep,k{j}));

         % Image transformation
         if strcmp(imgInfo.ColorType,'grayscale')
            % array of images
            DataSet{j}   = double(imresize(tempImage,[width height])); 
         else
            % array of images
            DataSet{j}   = double(imresize((tempImage),[width height])); 
         end
     end
 end
TestSet =  cell([], 1);
  for i=1:length(dir(fullfile(Testset,'*.jpg')))

     % Training set process
     k = dir(fullfile(Testset,'*.jpg'));
     k = {k(~[k.isdir]).name};
     for j=1:length(k)
        tempImage       = imread(horzcat(Testset,filesep,k{j}));
        imgInfo         = imfinfo(horzcat(Testset,filesep,k{j}));

         % Image transformation
         if strcmp(imgInfo.ColorType,'grayscale')
            % array of images
            TestSet{j}   = double(imresize(tempImage,[width height])); 
         else
            % array of images
            TestSet{j}   = double(imresize(tempImage,[width height])); 
         end
     end
  end

    % Prepare class label for first run of svm
% I have arranged labels 1 & 2 as per my convenience.

% It is always better to label your images numerically

% Note that for every image in our Dataset we need to provide one label.

% we have 10 images and we divided it into two label groups here.

    train_label               = zeros(size(10,1),1);
    train_label(1:4,1)   = 1;         % 1 = naa
    train_label(5:10,1)  = 2;         % 2 = ta


% Prepare numeric matrix for svmtrain

    Training_Set=[];
    for i=1:length(DataSet)
    b = imresize(DataSet{i},[100 100]);
    Training_Set_tmp= b(:);
    %Training_Set_tmp   = reshape(DataSet{i},1, 100*100);
    Training_Set=[Training_Set;Training_Set_tmp];
    end

    Test_Set=[];
    for j=1:length(TestSet)
    b = imresize(TestSet{j},[100 100]);
    Test_set_tmp= b(:);
    %Test_set_tmp   = reshape(TestSet{j},1, 100*100);
    Test_Set=[Test_Set;Test_set_tmp];
    end


% Perform first run of svm

SVMStruct = svmtrain(Training_Set, train_label, 'kernel_function', 'linear');
Group     = svmclassify(SVMStruct, Test_Set);

请指导我克服这一点。谢谢。

【问题讨论】：

您能否提供有关 Training_set 的更多信息？如果它是 PxPx3 RBG 图像的集合，我真的不认为这是您想要使用 svmtrain 的方式。在大多数情况下，您会从使用 PCA 之类的降维中受益，然后训练集将是 NxM，其中 N 是图像的数量，M 是减少的特征数量，因此 train_label 应该是 Nx1 或类似的东西。跨度>
Training_Set=[];对于 i=1:length(DataSet) b = imresize(DataSet{i},[100 100]); Training_Set_tmp= b(:); %Training_Set_tmp = reshape(DataSet{i},1, 100*100); Training_Set=[Training_Set;Training_Set_tmp];这就是我获取训练数据的方式
检查size(Training_Set,1)是否与length(train_label)相同。
您能否将评论中的代码放入问题中以获得完整的示例，显示问题的可运行代码？
@user3859472 您必须编辑您的问题并将该代码添加到其中（请正确格式化）。评论没用。

标签： matlab image-processing svm

【解决方案1】：

我最近遇到了这个类似的问题，经过几个小时的检查，我发现了这个问题：

% we have 10 images and we divided it into two label groups here.

train_label               = zeros(size(10,1),1);
train_label(1:4,1)   = 1;         % 1 = naa
train_label(5:10,1)  = 2;         % 2 = ta

我只有 499 张图片，而我将尺寸设置为 500（意外删除 1 张），从而导致与上述完全相同的错误。您没有在此处提供您的数据集，所以也许您可以考虑再次检查。

【讨论】：