This page lists a number of example VLFeat applications. The code can be found in the VLROOT/apps/ subdirectory in the VLFeat package.

# Basic recognition

This sample application uses VLFeat to train an test an image classifier on the Caltech-101 data. The classifier achieves 65% average accuracy by using a single feature and 15 training images per class. It uses:

• PHOW features (dense multi-scale SIFT descriptors)
• Elkan k-means for fast visual word dictionary construction
• Spatial histograms as image descriptors
• A homogeneous kernel map to transform a Chi2 support vector machine (SVM) into a linear one
• SVM classifiers

The program is fully contained in a single MATLAB M-file, and can also be simply adapted to use your own data (change conf.calDir).

This example application extends the Caltech-101 demo above in many ways: it supports multiple encoding methods, including BoVW, VLAD, and Fisher Vectors, tweaked image features, and multiple benchmark datasets. The code is located int apps/recognition. Start from the main file.

The following tables report results on a few standard benchmark datasets (PASCAL VOC 2007 classification challenge, Caltech 101 30 training images, MIT Scene 67, and Flickr Material Dataset) for a number of different encodings:

methodVOC07Caltech 101Scene 67FMD
FV59.12% mAP73.02% Acc58.25% Acc59.60% Acc
FV + aug.60.25% mAP75.61% Acc57.57% Acc60.80% Acc
FV + s.p.62.23% mAP77.63% Acc61.83% Acc60.80% Acc
VLAD + aug.54.66% mAP78.68% Acc53.29% Acc49.40% Acc
BOVW + aug.49.87% mAP75.98% Acc50.22% Acc46.00% Acc

The baseline feature is SIFT (vl_dsift) computed at seven scales with a factor $\sqrt{2}$ between successive scales, bins 8 pixel wide, and computed with a step of 4 pixels. All experiments but the Caltech-101 ones start by doubling the resolution of the input image. The details of the encodings are as follows:

• Bag-of-visual-words uses 4096 vector quantized visual words histogram square rooting, followed by $L^2$ normalization (Hellinger's kernel).
• VLAD uses 256 vector quantized visual words, signed square-rooting, component wise $L^2$ normalization, and global $L^2$ normalization (see vl_vlad).
• Fisher vectors uses a 256 visual words GMM and the improved formulation (signed square-rooting followed by $L^2$ normalization, see vl_fisher).
• Learning uses a linear SVM (see vl_svmtrain). The parameter $C$ is set to 10 for all dataset except PASCAL VOC, for which it is set to 1.
• Experiments labelled with “aug.” encode spatial information by appending the feature coordinates to the descriptor; the ones labelled with “s.p.” use a spatial pyramid with 1x1 and 3x1 subdivisions.

# SIFT mosaic

This sample application uses VLFeat to extract SIFT features form a pair of images and match them. It then filters the matches based on RANSAC and produces a mosaic. Read the code.