Bag of Words with HOG descriptors

Question

I'm not quite sure how to implement the "Bag of Words" approach with HOG descriptors. I've checked several sources which usually provide several steps to follow:

1. Compute the HOGs for the set of valid training images.
2. Apply an clustering algorithm to retrieve n centroids from the descriptors.
3. Perform some magic to create histograms with the frequency of the nearest centroids of the computed HOGs or use OpenCVs implementation to do this.
4. Train a linear SVM with the histograms

The step which involves magic (3) is not really clear. If I don't use OpenCV, how would I implement it?

The HOGs are vectors which are calculated cell-wise. So I have a vector for each cell. I could iterate over the vector and calculate the closest centroid for each element of the vector and create the histogram accordingly. Would this be a proper way to do it? But if so, I still have vectors of different sizes and no benefit from it.

Answer 1

Main steps can be expressed;

1- Extract features from your entire training set. (HOG feature for your aim)

2- Cluster those features into a vocabulary V; you get K distinct cluster centers.(K-Means, K-Medoid. Your hyperparameter will be K)

3- Encode each training image as a histogram of the number of times each vocabulary element shows up in the image. Each image is then represented by a length-K vector.

For example; first element of K maybe occurs 5 times, second element of K maybe occurs 10 times in your image. Doesn't matter at the end you will have a vector which has K elements.

K[0] = 5 k[1] = 10 .... .... K[n] = 3

4- Train the classifier using this vector. (Linear SVM)

When given a test image, extract the features. Now represent the test image as a histogram of the number of times each cluster center from V was closest to a feature in the test image. This is a length K vector again.