Kmeans and Bag of Generic Words

21 ●1 ●3 ●5

Hi guys,

i'm trying to implement my Bag of words with my own descriptor. I already did the kmeans part and it works perfectly. Now my question is which function in open CV I have to use to build the histogram and complete the training part of the code.

Consider that I used this code to compute the vocabulary:

Mat ComputeDictionary (Mat& features, const int K) {
int retries = 3;
int flags = KMEANS_PP_CENTERS;
Mat bestlabels, centers;
// K-Means function call    
kmeans(features, K, bestlabels, TermCriteria( TermCriteria::EPS+TermCriteria::COUNT,100,0.001), retries, flags, centers);
return centers;
}

Consider that my "features" matrix is a matrix with "number of samples * number of descriptors per sample " rows and "descriptor dimension" columns.

Now, I don't know how to proceed (I know the theory but I don't now how to implement it in c++)...could you please help me?

answered Apr 13 '17

berak
32993 ●7 ●81 ●312

updated Apr 13 '17

hmm, opencv uses feature matching, to compute the histograms

for your custom descriptor, i guess, you have to implement something similar on your own.

alternatively to the histograms as final features, you could store distances to the centers, or residuals even.

Comments

so, basically, I have to this:

build my own funtion to compute distances between each descriptor sample and the words of the vocabulary finding the minimum one.
Compute the histogram
Create the new feature matrix (samples x histogram dimension)
Use SVM chi squared to do training.

then the same for a test sample.

right?

alexMm1 (Apr 13 '17)edit

good plan ;)

there's norm(a,b) for the distance
make a 1d Mat histogram(1, nclusters, CV_32F, 0,0f), then just increase the bin: hist.at<float>(0, closest_cluster_id) += 1; it's also a good idea to normalize() it in the end.
start with an empty mat, and push_back() those histograms, one by one (on a single row)
i'd still prefer LINEAR, but that's up to experimenting on your side !

for the testsample it would just need steps 1. and 2.

berak (Apr 13 '17)edit

great thanks

alexMm1 (Apr 14 '17)edit

add a comment

Kmeans and Bag of Generic Words

1 answer

Comments

Links

Question Tools

Stats

Related questions

Kmeans and Bag of Generic Words edit savecancel

1 answer

Comments

Links

Question Tools

Stats

Related questions

Kmeans and Bag of Generic Words