Description

The package implements Improved Fisher Vectors as described in [1]. For a more concise description of Fisher Vectors see [2]. The functionality includes:

Fitting a Gaussian Mixture Model (GMM)
Determining the number of GMM components via BIC
Saving and loading the fitted GMM
Computing the (Improved) Fisher Vectors based on the fitted GMM

Installation

For intsallation via pip run the following command on your terminal (requires python 3.4 or higher):

$ pip install fishervector

afterwards you should be able to import the package in python:

from fishervector import FisherVectorGMM

First Steps

1. Simulate some data / get your data ready

We randomly sample data just for this tutorial -> use your own data e.g. SIFT features of images

import numpy as np
shape = [300, 20, 32] # e.g. SIFT image features
image_data = np.concatenate([np.random.normal(-np.ones(30), size=shape), np.random.normal(np.ones(30), size=shape)], axis=0)

2. Train the GMM

from fishervector import FisherVectorGMM
fv_gmm = FisherVectorGMM(n_kernels=10).fit(image_data)

Or alternatively fit the GMM using the BIC to determine the number of GMM components automatically:

from fishervector import FisherVectorGMM
fv_gmm = FisherVectorGMM().fit_by_bic(test_data, choices_n_kernels=[2,5,10,20])

3. Computing improved fisher vectors

image_data_test = image_data[:20] # use a fraction of the data to compute the fisher vectors
fv = fv_gmm.predict(image_data_test)

Contributors:

Jonas Rothfuss (https://github.com/jonasrothfuss/)
Fabio Ferreira (https://github.com/ferreirafabio/)

References: