Tutorials

This section features a number of tutorials illustrating some of the algorithms implemented in VLFeat, roughly divided into visual features such as SIFT and Fisher vectors and statistical methods, such as K-means, GMMs, KDTrees, and SVMs.

# Visual features

- Local features: the concept of frames (keypoints). An overview of the concept of feature frame used as geometric reference in feature detection.

- Covariant detectors. An introduction to computing co-variant features like Harris-Affine.

- Histogram of Oriented Gradients (HOG). Getting started with this ubiquitous representation for object recognition and detection.

- Local Intensity Order Pattern (LIOP). Getting started with the LIOP descriptor as an alternative to SIFT in keypoint matching.

- Maximally Stable Extremal Regions (MSER). Extracting MSERs from an image as an alternative covariant feature detector.

- Image distance transform. Compute the image distance transform for fast part models and edge matching.

- Fisher vector and VLAD encodings. Compute global image encodings by pooling local image features with Fisher vectors and VLAD.

# Statistical methods

- GMM. Learn Gaussian Mixture Models using the Expectation Maximization algorithm.

- k-means. Cluster features with k-means.

- Agglomerative Information Bottleneck (AIB). Cluster discrete data based on the mutual information between the data and class labels.

- Quick shift. An introduction which shows how to create superpixels using this quick mode seeking method.

- SLIC. An introduction to SLIC superpixels.

- Support Vector Machine (SVM). Learn a binary classifier and check its convergence by plotting various statistical information.

- Forests of kd-trees. Approximate nearest neighbour queries in high dimensions using an optimized forest of kd-trees.

- Plotting functions for rank evaluation. Learn how to plot ROC, DET, and precision-recall curves.

- MATLAB Utilities. A list of useful MATLAB functions bundled with VLFeat.

# Obsolete tutorials

- Integer optimized k-means (IKM). VLFeat integeger-otpimized k-means implementation (obsolete).

- Hierarchical k-means (HIKM). Create a fast k-means tree for integer data (obsolete).