Question

我想问你是否可以使用散列技术和SURF算法，我制作了一个程序，通过将测试图像与保存的图像数据集进行匹配来进行人脸识别。

我使用Accord.net并通过这个库的BOW制作了一些功能然后我制作了ID3决策树和KNN，但两种方式的结果都不是很好，我问我是否可以使用散列技术来快速制作更好的结果，或者这是不可行的？这是BOW的代码

                 private void button2_Click(object sender, EventArgs e)
    {
        try
        {
            var watchFEC = System.Diagnostics.Stopwatch.StartNew();
            Accord.Math.Random.Generator.Seed = 0;
            bow.ParallelOptions.MaxDegreeOfParallelism = 1;
            bow.Learn(DatasetImages);
            // After this point, we will be able to translate
            // images into double[] feature vectors using
            features = bow.Transform(DatasetImages);
            watchFEC.Stop();
            var elapsedMs = watchFEC.ElapsedMilliseconds;
            MessageBox.Show("Feature Extraction and Clastering is done" + '\n' + "Time for Feature Extraction and Clastering for Dataset is: " + elapsedMs.ToString() + "  ms");
        } catch { MessageBox.Show("Error"); }        }

这是学习的代码

private void button3_Click(object sender, EventArgs e)
    {
        try
        {
            var watchLearn = System.Diagnostics.Stopwatch.StartNew();
            inputs = features.ToInt32();
            tree = teacher.Learn(inputs, outputs);
            error = new ZeroOneLoss(outputs).Loss(tree.Decide(inputs));
            MessageBox.Show("Error rate of learning is : "+error.ToString());
            watchLearn.Stop();
            var elapsedMs = watchLearn.ElapsedMilliseconds;
            MessageBox.Show("Learning is done" + '\n' + "Time for Learning is: " + elapsedMs.ToString() + "  ms");
        }
        catch(Exception ex) { MessageBox.Show("Error"+ex); }

    }

以及此测试代码

      private void button4_Click_1(object sender, EventArgs e)
    {
        try
        {
            var watchTest = System.Diagnostics.Stopwatch.StartNew();
            Bitmap[] testimage = new Bitmap[1];
            testimage[0] = (Bitmap)pictureBox1.Image;
            var ff = bow.Transform(testimage);
            ff.ToInt32();
            var predicted = tree.Decide(ff);
            int i = 1;
            for (i = 1; i < sizeofdataset; i++)
            {
                if (predicted[0] == Convert.ToInt16(workSheet.Cells[i, 3].Value.ToString()))
                {

                    listBox1.SelectedItem = i;
                    MessageBox.Show("Test" + i);
                    break;
                }
            }
            MessageBox.Show("Test" + predicted[0]);
            pictureBox2.Image = new Bitmap(workSheet.Cells[i, 1].Value.ToString());
            watchTest.Stop();
            var elapsedMs = watchTest.ElapsedMilliseconds;
            MessageBox.Show("Time for Testing is: " + elapsedMs.ToString() + "  ms");
        }

        catch (Exception ex) { MessageBox.Show("Error" + ex); }

    }

Answer 1

请尝试使用带有卡方内核的SVM，而不是ID3或k-NN。

如果您想尝试使用SVM，可以使用example on how to create multi-class kernel SVMs at the bottom of this page (see second example)。你可以用“ChiSquare”替换它所写的“高斯”的所有地方，以便创建一个卡方SVM。

如果您碰巧在{“索引超出数组的范围内。”}正如您在项目的问题跟踪器中指出的那样，我认为您可能没有培训或测试样本。请确保您有足够的训练样本用于所有类，您的班级编号从0开始，输出向量中的最高级别标签对应于number_of_classes - 1，并且在此区间内没有任何相关训练样本的整数。

我发布了一个关于如何使用Accord.NET框架中的卡方内核训练SVM的示例：

// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
// 
double[][] inputs =
{
    //               input         output
    new double[] { 0, 1, 1, 0 }, //  0 
    new double[] { 0, 1, 0, 0 }, //  0
    new double[] { 0, 0, 1, 0 }, //  0
    new double[] { 0, 1, 1, 0 }, //  0
    new double[] { 0, 1, 0, 0 }, //  0
    new double[] { 1, 0, 0, 0 }, //  1
    new double[] { 1, 0, 0, 0 }, //  1
    new double[] { 1, 0, 0, 1 }, //  1
    new double[] { 0, 0, 0, 1 }, //  1
    new double[] { 0, 0, 0, 1 }, //  1
    new double[] { 1, 1, 1, 1 }, //  2
    new double[] { 1, 0, 1, 1 }, //  2
    new double[] { 1, 1, 0, 1 }, //  2
    new double[] { 0, 1, 1, 1 }, //  2
    new double[] { 1, 1, 1, 1 }, //  2
};

int[] outputs = // those are the class labels
{
    0, 0, 0, 0, 0,
    1, 1, 1, 1, 1,
    2, 2, 2, 2, 2,
};

// Create the multi-class learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning<ChiSquare>()
{
    // Configure the learning algorithm to use SMO to train the
    //  underlying SVMs in each of the binary class subproblems.
    Learner = (param) => new SequentialMinimalOptimization<ChiSquare>()
    {
        // Estimate a suitable guess for the Gaussian kernel's parameters.
        // This estimate can serve as a starting point for a grid search.
        UseKernelEstimation = true
    }
};

// Configure parallel execution options (or leave it at the default value for maximum speed)
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;

// Learn a machine
var machine = teacher.Learn(inputs, outputs);

// Obtain class predictions for each sample
int[] predicted = machine.Decide(inputs);

// Get class scores for each sample
double[] scores = machine.Score(inputs);

// Compute classification error
double error = new ZeroOneLoss(outputs).Loss(predicted);

SURF与散列

1 个答案: