`skimage.feature._fisher_vector:fisher_vector`

source: /dev/scikit-image/src/skimage/feature/_fisher_vector.py :148

Signature

   def     fisher_vector (    descriptors    ,    gmm    ,  * ,     improved    =  False   ,    alpha    =  0.5     )

Summary

Compute the Fisher vector given some descriptors/vectors, and an associated estimated GMM.

Parameters

descriptors : np.ndarray, shape=(n_descriptors, descriptor_length): NumPy array of the descriptors for which the Fisher vector representation is to be computed.
gmm : :class:`sklearn.mixture.GaussianMixture`: An estimated GMM object, which contains the necessary parameters needed to compute the Fisher vector.
improved : bool, default=False: Flag denoting whether to compute improved Fisher vectors or not. Improved Fisher vectors are L2 and power normalized. Power normalization is simply f(z) = sign(z) pow(abs(z), alpha) for some 0 <= alpha <= 1.
alpha : float, default=0.5: The parameter for the power normalization step. Ignored if improved=False.

Returns

fisher_vector : np.ndarray: The computation Fisher vector, which is given by a concatenation of the gradients of a GMM with respect to its parameters (mixture weights, means, and covariance matrices). For D-dimensional input descriptors or vectors, and a K-mode GMM, the Fisher vector dimensionality will be 2KD + K. Thus, its dimensionality is invariant to the number of descriptors/vectors.

Examples

from skimage.feature import fisher_vector, learn_gmm
sift_for_images = [np.random.random((10, 128)) for _ in range(10)]
num_modes = 16
gmm = learn_gmm(sift_for_images, n_modes=num_modes)
test_image_descriptors = np.random.random((25, 128))
fv = fisher_vector(test_image_descriptors, gmm)

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Aliases

skimage.feature.fisher_vector