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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | % VL_FISHER Fisher vector feature encoding
% ENC = VL_FISHER(X, MEANS, COVARIANCES, PRIORS) computes the
% Fisher vector encoding of the vectors X relative to the Gaussian
% mixture model with means MEANS, covariances COVARIANCES, and pror
% mode probabilities PRIORS.
%
% X has one column per data vector (e.g. a SIFT descriptor), and
% MEANS and COVARIANCES one column per GMM component (covariance
% matrices are assumed diagonal). PRIORS has size equal to the
% number of GMM components. All data must be of the smae class,
% either SINGLE or DOUBLE.
%
% ENC is a vector of the same class of X of size equal to the
% product of the data dimension and the number of components.
%
% By default, the standard Fisher vector is computed. VL_FISHER()
% accepts the following options:
%
% Normalized::
% If specified, L2 normalize the Fisher vector.
%
% SquareRoot::
% If specified, the signed square root function is applied to
% ENC before normalization.
%
% Verbose::
% Increase the verbosity level (may be specified multiple times).
%
% See: <a href="matlab:vl_help('fisher')">Fisher vectors</a>, VL_HELP().
% Authors: David Novotny, Andrea Vedaldi
% Copyright (C) 2013 David Novotny and Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
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