Applications

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).

# Advanced encodings for recognition

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:

method | VOC07 | Caltech 101 | Scene 67 | FMD
--- | --- | --- | --- | ---
FV | 59.12% mAP | 73.02% Acc | 58.25% Acc | 59.60% Acc
FV + aug. | 60.25% mAP | 75.61% Acc | 57.57% Acc | 60.80% Acc
FV + s.p. | 62.23% mAP | 77.63% Acc | 61.83% Acc | 60.80% Acc
VLAD + aug. | 54.66% mAP | 78.68% Acc | 53.29% Acc | 49.40% Acc
BOVW + aug. | 49.87% mAP | 75.98% Acc | 50.22% Acc | 46.00% 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.