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

Question

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

Answer 1

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)