Local feature aggregation

This is a library that implements methods to aggregate local features (mainly for multimedia) into a single global feature that can be used easily with any classifier.

Dependencies

The library depends on scikit-learn and all the feature aggregation methods extend the scikit-learn BaseEstimator class.

Example

.. code:: python

import numpy as np
from feature_aggregation import BagOfWords, FisherVectors

X = np.random.rand(1000, 2)
bow = BagOfWords(10)
fv = FisherVectors(10)

bow.fit(X)
fv.fit(X)

G1 = bow.transform(np.random.rand(10, 100, 2))
G2 = fv.transform([
    np.random.rand(int(np.random.rand()*100), 2) for _ in range(10)
])

A more complex example using OpenCV to extract dense SIFT and then transform them using Bag Of Words and train an SVM with chi square additive kernel.

.. code:: python

import numpy as np
import cv2
from sklearn.datasets import fetch_olivetti_faces
from sklearn.kernel_approximation import AdditiveChi2Sampler
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC

from feature_aggregation import BagOfWords

def sift(*args, **kwargs):
    try:
        return cv2.xfeatures2d.SIFT_create(*args, **kwargs)
    except:
        return cv2.SIFT()

def dsift(img, step=5):
    keypoints = [
        cv2.KeyPoint(x, y, step)
        for y in range(0, img.shape[0], step)
        for x in range(0, img.shape[1], step)
    ]
    features = sift().compute(img, keypoints)[1]
    features /= features.sum(axis=1).reshape(-1, 1)
    return features

# Generate dense SIFT features
faces = fetch_olivetti_faces()
features = [
    dsift((x.reshape(64, 64, 1)*255).astype(np.uint8))
    for x in faces.data
]

# Aggregate those features with bag of words using online training
bow = BagOfWords(100)
for i in range(2):
    for j in range(0, len(features), 10):
        bow.partial_fit(features[j:j+10])
faces_bow = bow.transform(features)

# Split in training and test set
train = np.arange(len(features))
np.random.shuffle(train)
test = train[200:]
train = train[:200]

# Train and evaluate
svm = Pipeline([("chi2", AdditiveChi2Sampler()), ("svm", LinearSVC(C=10))])
svm.fit(faces_bow[train], faces.target[train])
print(classification_report(faces.target[test], svm.predict(faces_bow[test])))