A computer vision practical by the Oxford Visual Geometry group, authored by Andrea Vedaldi and Andrew Zisserman.

Start from doc/instructions.html.

The practical consists of four exercies, organized in the following files:

• exercise1.m -- Part I: Sparse features for matching specific objects in images
• exercise2.m -- Part II: Affine co-variant detectors
• exercise3.m -- Part III: Towards large scale retrieval
• exercise4.m -- Part IV: Large scale retrieval

The computer vision algorithms are implemented by VLFeat. This package contains the following MATLAB functions:

• findNeighbours.m: Match features based on their descriptors.
• geometricVerification.m: Geometrically verify feature matches.
• getFeatures.m: Extract features from an image.
• getHistogramFromImage.m: Get a feature hisotgram from an image.
• getHistogram.m: Get a feature histogram from quantised features.
• loadIndex.m: Load an image datbase with an inverted index.
• plotbox.m: Plot boxes.
• plotMatches.m: Plot feature matches.
• plotRetrievedImages.m: Plot search results.
• plotQueryImage.m: Plot the query image for a set of search results.
• search.m: Search an image database.
• setup.m: Setup MALTAB to use the required libraries.

1. From Bash, run ./extras/bootstrap.sh. This will download the Oxford 5k practical data.
2. From MATLAB, run addpath extras ; preprocess. This will download the VLFeat library (http://www.vlfeat.org) and compute a visual index for the Oxford 5k data.
3. From MATALB, run addpath extras ; preprocess_paintings. This will download and index a number of painting images from Wikipedia.
4. From Bash, run make -f extra/Makefile pack. This will pack the practical in archives that can be redestributed to students.

• 2015a - Compatibility with MATLAB 2014b.
• 2014a - Improves documentation and packing.
• 2013a - Improves documentation, uses last version of VLFeat, bugfixes.
• 2012 - Adds co-varaint feature detectors
• 2011 - Initial edition

Copyright (c) 2011-13 Andrea Vedaldi and Andrew Zisserman

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