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BagOfWords.m
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BagOfWords.m
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%BagOfWords Bag of words class
%
% The BagOfWords class holds sets of features for a number of images and
% supports image retrieval by comparing new images with those in the 'bag'.
%
% Methods::
% isword Return all features assigned to word
% occurrences Return number of occurrences of word
% remove_stop Remove stop words
% wordvector Return word frequency vector
% wordfreq Return words and their frequencies
% similarity Compare two word bags
% contains List the images that contain a word
% exemplars Display examples of word support regions
% display Display the parameters of the bag of words
% char Convert the parameters of the bag of words to a string
%
% Properties::
% K The number of clusters specified
% nstop The number of stop words specified
% nimages The number of images in the bag
%
% Reference::
%
% J.Sivic and A.Zisserman,
% "Video Google: a text retrieval approach to object matching in videos",
% in Proc. Ninth IEEE Int. Conf. on Computer Vision, pp.1470-1477, Oct. 2003.
%
% See also PointFeature.
% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% MVTB is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU Lesser General Public License for more details.
%
% You should have received a copy of the GNU Leser General Public License
% along with MVTB. If not, see <http://www.gnu.org/licenses/>.
classdef BagOfWords < handle
properties
features % vector of all features in the bag (PointFeature class)
K % number of clusters
C % cluster centres (NDxNW)
words % vector of word indices (NW)
nstop % number of stop words
stopwords % list of stop words
map % maps word index with stop words to word index without stop words
nimages % number of images (NI)
wv % cached word vectors
end
methods
function bag = BagOfWords(sf, a1)
%BagOfWords.BagOfWords Create a BagOfWords object
%
% B = BagOfWords(F, K) is a new bag of words created from the feature
% vector F and with K words. F can also be a cell array, as produced
% by ISURF() for an image sequence.
%
% The features are sorted into K clusters and each cluster is termed
% a visual word.
%
% B = BagOfWords(F, B2) is a new bag of words created from the feature
% vector F but clustered to the words (and stop words) from the existing
% bag B2.
%
% Notes::
% - Uses the MEX function vl_kmeans to perform clustering (vlfeat.org).
%
% See also PointFeature, ISURF.
% save the feature vector
if iscell(sf)
bag.features = [sf{:}];
else
bag.features = sf;
end
bag.nimages = max([bag.features.image_id]);
if isnumeric(a1)
K = a1;
% do the clustering
[bag.C,L] = vl_kmeans([bag.features.descriptor], K, ...
'verbose', 'algorithm', 'elkan');
bag.K = K;
bag.words = double(L);
elseif isa(a1, 'BagOfWords')
oldbag = a1;
% cluster using number of words from old bag
bag.words = closest([bag.features.descriptor], oldbag.C);
if oldbag.stopwords > 0
% remove stopwords as per the original bag file
bag.K = oldbag.K;
bag.stopwords = oldbag.stopwords;
k = find(ismember(bag.words, oldbag.stopwords));
fprintf('Removing %d features associated with stop words\n', length(k));
bag.words(k) = [];
bag.words = oldbag.map(bag.words);
bag.features(k) = [];
end
bag.compute_wv(oldbag);
end
end
function f = isword(bag, words)
%BagOfWords.isword Features from words
%
% F = B.isword(W) is a vector of feature objects that are assigned to any of
% the word W. If W is a vector of words the result is a vector of features
% assigned to all the words in W.
k = ismember(bag.words, words);
f = bag.features(k);
end
function n = occurrence(bag, word)
%BagOfWords.occurrence Word occurrence
%
% N = B.occurrence(W) is the number of occurrences of the word W across
% all features in the bag.
n = sum(bag.words == word);
end
function [all2, S] = remove_stop(bag, nstop)
%BagOfWords.remove_stop Remove stop words
%
% B.remove_stop(N) removes the N most frequent words (the stop words)
% from the bag. All remaining words are renumbered so that the word
% labels are consecutive.
[w,f] = count_unique(bag.words);
[f,i] = sort(f, 'descend');
bag.stopwords = w(i(1:nstop));
% remove all features that are stop words from L and all
k = find(ismember(bag.words, bag.stopwords));
fprintf('Removing %d features associated with %d most frequent words\n', ...
length(k), nstop);
% fix the labels
b = zeros(1,length(bag.words));
b(bag.stopwords) = 1;
bag.map = [1:length(bag.words)] - cumsum(b);
bag.words(k) = [];
bag.words = bag.map(bag.words);
bag.features(k) = [];
end
function wv = wordvector(bag, k)
%BagOfWords.wordvector Word frequency vector
%
% WF = B.wordvector(J) is the word frequency vector for the J'th image
% in the bag. The vector is Kx1 and the angle between any two WFVs is
% an indication of image similarity.
%
% Notes::
% - The word vector is expensive to compute so a lazy evaluation is
% performed on the first call to this function
if isempty(bag.wv)
bag.compute_wv();
end
if nargin > 1
wv = bag.wv(:,k);
else
wv = bag.wv;
end
end
% compute image-word frequency
function W = iwf(bag)
N = bag.nimages; % number of images
% Create the word frequency matrix W
% column correspond to images
% row correspond to words
% each element is the number of occurences of that word in that iamge
W = [];
id = [bag.features.image_id];
nl = bag.K - length(bag.stopwords);
for i=1:bag.nimages
% get the words associated with image i
words = bag.words(id == i);
% create columns of the W
[w,f] = count_unique(words);
v = zeros(nl,1);
v(w) = f;
W = [W v];
end
end
function W = compute_wv(bag, bag2)
if nargin == 2
Wv = bag2.iwf();
N = bag2.nimages;
W = bag.iwf();
else
Wv = bag.iwf();
N = bag.nimages;
W = Wv;
end
Ni = sum( Wv'>0 );
m = [];
for i=1:bag.nimages
% number of words in this image
nd = sum( W(:,i) );
% word occurrence frequency
nid = W(:,i)';
v = nid/nd .* log(N./Ni);
v(~isfinite(v)) = 0;
m = [m v'];
end
if nargout == 1
W = m;
else
bag.wv = m;
end
end
function [w,f] = wordfreq(bag)
%BagOfWords.wordfreq Word frequency statistics
%
% [W,N] = B.wordfreq() is a vector of word labels W and the corresponding
% elements of N are the number of occurrences of that word.
[w,f] = count_unique(bag.words);
end
% compute similarity matrix
function sim = similarity(bag1, bag2)
wv1 = bag1.wordvector;
wv2 = bag2.wordvector;
for i=1:bag1.nimages
for j=1:bag2.nimages
v1 = wv1(:,i); v2 = wv2(:,j);
sim(i,j) = dot(v1,v2) / (norm(v1) * norm(v2));
end
end
end
function display(bag)
%BagOfWords.display Display value
%
% B.display() displays the parameters of the bag in a compact human
% readable form.
%
% Notes::
% - This method is invoked implicitly at the command line when the result
% of an expression is a BagOfWords object and the command has no trailing
% semicolon.
%
% See also BagOfWords.char.
loose = strcmp( get(0, 'FormatSpacing'), 'loose');
if loose
disp(' ');
end
disp([inputname(1), ' = '])
if loose
disp(' ');
end
disp(char(bag))
if loose
disp(' ');
end
end
function s = char(bag)
%BagOfWords.char Convert to string
%
% S = B.char() is a compact string representation of a bag of words.
s = sprintf(...
'BagOfWords: %d features from %d images\n %d words, %d stop words\n', ...
length(bag.features), bag.nimages, ...
bag.K-length(bag.stopwords), length(bag.stopwords));
end
function v = contains(bag, word)
%BagOfWords.contains Find images containing word
%
% K = B.contains(W) is a vector of the indices of images in the sequence that
% contain one or more instances of the word W.
v = unique([bag.isword(word).image_id]);
end
function out = exemplars(bag, words, images, varargin)
%BagOfWords.exemplars Display exemplars of words
%
% B.exemplars(W, IMAGES, OPTIONS) displays examples of the support regions of
% the words specified by the vector W. The examples are displayed as a table
% of thumbnail images. The original sequence of images from which the features
% were extracted must be provided as IMAGES.
%
% IM = B.exemplars(W, IMAGES, OPTIONS) as above but returns the thumbnails
% as a composite image.
%
% Options::
% 'columns',N Number of columns to display (default 10)
% 'maxperimage',M Maximum number of exemplars to display from any
% one image (default 2)
% 'width',W Width of each thumbnail [pixels] (default 50)
% 'label' Display word labels on the thumbnails.
opt.gap = 2;
opt.columns = 10;
opt.maxperimage = 2;
opt.width = 50;
opt.label = false;
opt.rows = [];
opt = tb_optparse(opt, varargin);
% figure the number of exemplars to show, no more than opt.maxperimage
% from any one image
nexemplars = 0;
exemplars = {};
for w=words
image_prev = [];
count = 0;
for f=bag.isword(w)
if f.image_id == image_prev
count = count + 1;
if count > opt.maxperimage
continue;
end
end
exemplars = [exemplars {{w, f}}];
end
end
if isempty(opt.rows)
nr = ceil( length(exemplars) / opt.columns);
else
nr = opt.rows;
end
nc = min(length(exemplars), opt.columns);
n = min(length(exemplars), nr*nc);
exemplars = exemplars(1:n);
Ng = opt.width+opt.gap;
composite = ones(nr*Ng, nc*Ng);
% render the support regions into composite image
row = 0; col = 0;
for ex=exemplars
ex = ex{1};
word = ex{1}; f = ex{2};
% extract it from the containing image
support = f.support(images, opt.width);
% paste it into the panel
composite = ipaste(composite, support, [col row]*Ng, 'zero');
% update row/column indices
col = col + 1;
if col >= opt.columns
row = row + 1;
col = 0;
end
end
if nargout == 1
% output specified, return the image
out = composite;
else
if opt.label
% no output specified, optionally label the cells
if nargout == 0
idisp(composite, 'plain');
end
row = 0; col = 0;
for ex=exemplars
ex = ex{1};
word = ex{1}; f = ex{2};
text(col*Ng+opt.gap*2, row*Ng+3*opt.gap, ...
sprintf('%d #%d', word, f.image_id), 'Color', 'g')
% update row/column indices
col = col + 1;
if col >= opt.columns
row = row + 1;
col = 0;
end
end
else
idisp(composite, 'plain');
end
end
end
end
end