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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 49 50 | % VL_VLAD VLAD feature encoding
% ENC = VL_VLAD(X, MEANS, ASSIGNMENTS) computes the VLAD
% encoding of the vectors X relative to cluster centers MEANS and
% vector-to-cluster soft assignments ASSIGNMENTS.
%
% X has one column per data vector (e.g. a SIFT descriptor), and
% MEANS has one column per component. Usually one has one component
% per KMeans cluster and MEANS are the KMeans centers. X and MEANS
% have the same number of rows and the data class, which can be
% either SINGLE or DOUBLE.
%
% ASSIGNMENTS has as many rows as clusters and as many columns as
% X. Its columns are non-negative and should sum to one,
% representing the soft assignment of the corresponding vector in X
% to each of the clusters. It is of the same class as X.
%
% ENC is a vector of the same class of X of size equal to the
% product of the data dimension and the number of clusters.
%
% By default, ENC is L2 normalized. VL_VLAD() accepts the following
% options:
%
% Unnormalized::
% If specified, no overall normalization is applied to ENC.
%
% NormalizeComponents::
% If specified, the part of the encoding corresponding to each
% cluster is individually normalized.
%
% NormalizeMass::
% If specified, each component is re-normalized by the mass
% of data vectors assigned to it. If NormalizedComponents is
% also selected, this has no effect.
%
% 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('vlad')">VLAD</a>, VL_HELP().
% Authors: David Novotny and 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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