CODEWITHSUNDEEP
opencvimage-processingclassificationsift
madan ram
madan ram

Reputation: 1270

SIFT clustering converting sift features (128 dimensional vector) into a vocabulary

How to cluster the extracted SIFT descriptors. The aim of doing clustering is to use it for classification purpose.

Upvotes: 1

Views: 2656

Answers (3)

madan ram
madan ram

Reputation: 1270

To cluster , convert N*128 dimension(N is the number of descriptor from each image) into a array of M*128 dimension (M number of descriptor from all images). and perform cluster on this data.

eg:

def dict2numpy(dict):
    nkeys = len(dict)
    array = zeros((nkeys * PRE_ALLOCATION_BUFFER, 128))
    pivot = 0
    for key in dict.keys():
        value = dict[key]
        nelements = value.shape[0]
        while pivot + nelements > array.shape[0]:
            padding = zeros_like(array)
            array = vstack((array, padding))
        array[pivot:pivot + nelements] = value
        pivot += nelements
    array = resize(array, (pivot, 128))
    return array

all_features_array = dict2numpy(all_features)
nfeatures = all_features_array.shape[0]
nclusters = 100
codebook, distortion = vq.kmeans(all_features_array,
                                         nclusters)

Upvotes: 0

skm
skm

Reputation: 5649

Approach:

  • First of all compute the SIFT descriptor for each image/object and then push_back that descriptor into a single image (lets called that image Mat featuresUnclustered).

  • After that your task is to cluster all the descriptor into some number of groups/clusters (which is decided by you). That will be the size of your vocabulary/dictionary.

    int dictionarySize=200;

  • And then finally comes the step of clustering them

    //define Term Criteria
TermCriteria tc(CV_TERMCRIT_ITER,100,0.001);

//retries number
int retries=1;

//necessary flags
int flags=KMEANS_PP_CENTERS;

//Create the BoW (or BoF) trainer
BOWKMeansTrainer bowTrainer(dictionarySize,tc,retries,flags);

//cluster the feature vectors
Mat dictionary=bowTrainer.cluster(featuresUnclustered);

Upvotes: 2

michaeltang
michaeltang

Reputation: 2898

usually kmeans is applied to get k centers, you can change each image into a vector of the K (each dimension represent how many patch in that cluster).

Upvotes: -1

Related Questions