Question

根据我的理解,我给出假冒代码中的算法:

# learning phase of Viola Jones
foreach feature # these are the pattern, see figure 1, page 139
    # these features are moved over the entire 24x24 sample pictures
    foreach (x,y) so that the feature still matches the 24x24 sample picture
        # the features are scaled over the window from [(x,y) - (24,24)]
        foreach scaling of the feature
            # calc the best threshold for a single, scaled feature
            # for this, the feature is put over each sample image (all 24x24 in the paper)
            foreach positive_image
                thresh_pos[this positive image] := HaarFeatureCalc(position of the window, scaling, feature)
            foreach negative_image
                thresh_neg[this negative image] := HaarFeatureCalc(position of the window, scaling, feature)
            #### what s next?
            #### how do I use the thresholds (pos / neg)?

这是本SO问题中的前线:。 Viola-Jones v. 180k characteristics

这一算法称为“国土功能”,我认为我的理解是:

function: HaarFeatureCalc
    threshold := (sum of the pixel in the sample picture that are white in the feature pattern) -
        (sum of the pixel in the sample picture that are grey in the feature pattern)
    # this is calculated with the integral image, described in 2.1 of the paper
    return the threshold

至今没有任何错误?

Viola Jones的学习阶段,基本发现哪些特征/导师是决定最多的。我不理解文件描述的AdaBoost是如何运作的。

www.un.org/spanish/ecosoc 问题:文件中的AdaBoost如何看待代号法中的内容?

Answer 1

Viola Jones:

First you will hard code 180k classifiers(feartures)
And initially all the training data will have same weights
Now each classifier(feature) will be applied to every training data you have
Each feature will classify your training data as either a face or not a face
based on this classifications for every feature you will calculate the error
After calculating the error of each feature, you pick the feature with error that is most far away from 50% by which i mean if you have one feature with 90% error and other feature with 20% error then you will select the feature with 90% error and add it to the strong classififer
You will update weights of each training data
Now you will repeat the process untill you get a nice accuracy for you validation data with the strong classifier that you have built
AdaBoost is a technique of making a strong classifiers out of weak classifier

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