Local feature aggregation methods for multimedia
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feature_aggregation
.gitignore
LICENSE
README.rst
setup.py

README.rst

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

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.

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])))