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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 38 39 40 41 42 43 44 45 46 47 48 | % 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 prior 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, hence these are simply the variance
% of each data dimension). PRIORS has size equal to the number of
% GMM components. All data must be of the same 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.
%
% Improved::
% If specified, compute the improved variant of the Fisher
% Vector. This is equivalent to specifying the Normalized and
% SquareRoot options.
%
% Fast::
% If specified, uses slightly less accurate computations but
% significantly increase the speed in some cases (particularly
% with a large number of Gaussian modes).
%
% 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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