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matlab code for feature extraction
In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Feature extraction is related to dimensionality reduction.
When the input data to an algorithm is too large to be processed and it is suspected to be redundant (e.g. the same measurement in both feet and meters, or the repetitiveness of images presented as pixels), then it can be transformed into a reduced set of features (also named a features vector). This process is called feature extraction. The extracted features are expected to contain the relevant information from the input data, so that the desired task can be performed by using this reduced representation instead of the complete initial data.
The goal of this toolbox is to simplify the process of feature extraction, of commonly used computer vision features such as HOG, SIFT, GIST and Color, for tasks related to image classification. The details of the included features are available in FEATURES.md.
In addition to providing some of the popular features, the toolbox has been designed for use with the ever increasing size of modern datasets - the processing is done in batches and is fully parallelized on a single machine (using parfor), and can be easily distributed across multiple machines with a common file system (the standard cluster setup in many universities).
The features extracted in a bag-of-words manner ('color', 'hog2x2', 'hog3x3', 'sift', 'ssim') are encoded using Locality-Constrained Linear Coding to allow the use of a linear classifier for fast training + testing.
In my experients, I have found 'hog2x2' or 'hog3x3' to be most effective as global image features, and tend to perform even better when combined with 'color' features which contain complementary information.
The toolbox works on Matlab and Octave. Octave may still have some compatibility issues though and doesn't support paralell processing.
Installation
Before you can use the code, you need to download this repository and compile the mex code:
$ git clone http://githubadikhosla/feature-extraction
$ cd feature-extraction
$ matlab
>> compile
To the best of my knowledge, there should be no issues compiling on Linux, Mac or Windows. Octave currently isn't able to compile, but most features should be working.
Basic usage
The basic usage is relatively simple:
>> addpath(genpath('.'));
>> datasets = {'pascal', 'sun'}; % specify name of datasets
>> train_lists = {{'pascal1.jpg'}, {'sun1.jpg', 'sun2.jpg'}}; % specify lists of train images
>> test_lists = {{'pascal2.jpg', 'pascal3.jpg'}, {'sun3.jpg'}}; % specify lists of test images
>> feature = 'hog2x2'; % specify feature to use
>> c = conf(); % load the config structure
>> datasets_feature(datasets, train_lists, test_lists, feature, c); % perform feature extraction
>> train_features = load_feature(datasets{1}, feature, 'train', c); % load train features of pascal
>> test_features = load_feature(datasets{2}, feature, 'test', c); % load test features of sun
The list of available features is:
'color', 'gist', 'hog2x2', 'hog3x3', 'lbp', 'sift', 'ssim'
Details are given here. The datasets_feature function can be run on multiple machines in parallel to speed up feature extraction. This function handles the complete pipeline of building a dictionary (for bag-of-words features), coding features to the dictionary, and pooling them together in a spatial pyramid.
You can use a single or multiple datasets as shown above. A seperate folder will be created for each dataset and a different dictionary will be learned, unless specified otherwise in the configuration structure.
Demo
There is a demo script provided in this code to extract color features on a provided set of train and test images. Then the features are used in a nearest neighbor classifier to predict the class of the test images.
>> demo
The demo above will show the train and test images, and the nearest neighbors of the test images from the training set.
Config structure
There are various options available through the config structure created using the conf() function:
cache: main folder where all the files will be stored
feature_config.(feature_name): contains the configuration of feature_name such as dictionary size
batch_size: batch size for feature processing (reduce for less RAM usage)
cores: specify number of cores to use for parfor (0 = use all)
verbosity: used to change how much is output to screen during feature computation (0 = low, 1 = high)
common_dictionary: used to share a common dictionary across datasets. Dictionary is learned using equal number of samples from each dataset (useful for ECCV 2012 paper).
Additional options are described in conf.m.
Bundled code
There are functions or snippets of code included with or without modification from the following packages:
Feature coding: Locality-constrained linear coding
Pixelwise HOG/Gist: LabelMe Toolbox
For color features: Color Naming
SIFT features: Spatial pyramid matching code
LBP features: LBP Matlab code
SSIM: VGG SSIM package
Fast k-means
