This page lists a number of sample VLFeat applications. Their code
can be found in the
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).
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.