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This short tutorial shows how to compute Fisher vector and VLAD encodings with VLFeat MATLAB interface.

These encoding serve a similar purposes: summarising in a vectorial statistic a number of local feature descriptors (e.g. SIFT). Similarly to bag of visual words, they compare local descriptor to a dictionary, obtained with vectonr quantization (KMeans) in the case of VLAD and Gaussina Mixture Models for Fisher Vectors. However, rather than storing visual word occurences only, these representation store the difference between dictonary elements and pooled local features.

# Fisher encoding

The Fisher encoding uses GMM to construct a visual word dicionary. To exemplify constructing a GMM, we consider a number of 2 dimensional data points (see also the GMM tutorial). In practice, these would be a collection of SIFT or other local image features:

numFeatures = 5000 ; dimension = 2 ; data = rand(dimension,numFeatures) ; numClusters = 30 ; [means, covariances, priors] = vl_gmm(data, numClusters);

Next we create another random set of vectors, which should be encoded using the Fisher vector representation and the GMM just obtained.

numDataToBeEncoded = 1000; dataToBeEncoded = rand(dimension,numDataToBeEncoded);

The Fisher vector encoding `enc` of these vectors is obtained by calling the `vl_fisher` function using the output of the `vl_gmm` function:

encoding = vl_fisher(datatoBeEncoded, means, covariances, priors);

The `encoding` vector is the Fisher vector representation of the data `dataToBeEncoded`.

Note that Fihser vectors support several normalization options that can affect substantially the performance of the representation.

# VLAD encoding

The Vector of Linearly Agregated Descriptors is similar to Fisher vectors but (i) it does not store second-order information about the features and (ii) it typically use KMeans instead of GMMs to generate the feature vocabulary (although the latter is also an option).

Consider the same 2D data matrix `data` used in the previous section to train the Fisher vector representation. To compute VLAD, we first need to obtain a visual word dictionary. This time, we use K-means:

numClusters = 30 ; centers = vl_kmeans(dataLearn, numClusters);

Now consider the data `dataToBeEncoded` and use the `vl_vlad` function to compute the encoding. Differently from `vl_fhiser`, `vl_vlad` requires the data-to-cluster assignments to be passed in. This allows using a fast vector quantization technique (e.g. kd-tree) as well as switching from soft to hard assignment.

In this example, we use a kd-tree for quantization:

kdtree = vl_kdtreebuild(centers) ; nn = vl_kdtreequery(kdtree, centers, dataEncode) ;

Now we have in the `nn` the indeces of the nearest center to each vector in the matrix `dataToBeEncoded`. The next step is to create an assignment matrix:

assignments = zeros(numClusters,numDataToBeEncoded); assignments(sub2ind(size(assignments), nn, 1:length(nn))) = 1;

It is now possible to encode the data using the `vl_vlad` function:

enc = vl_vlad(dataToBeEncoded,centers,assignments);

Note that, similarly to Fisher vectors, VLAD supports several normalization options that can affect substantially the performance of the representation.