Applications

This page lists a number of sample VLFeat applications. Their code can be found in the VLROOT/apps/ subdirectory.

# Caltech-101 classification

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
• An internal SVM (based on PEGASOS) for classification

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).

# 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.