Use the Computer Vision Toolbox™ functions for image category classification by creating a bag of visual words. The process generates a histogram of visual word occurrences that represent an image. These histograms are used to train an image category classifier. The steps below describe how to setup your images, create the bag of visual words, and then train and apply an image category classifier.
Organize and partition the images into training and test subsets. Use the
imageDatastore function to store images to use for training an image
classifier. Organizing images into categories makes handling large sets of images much
easier. You can use the
splitEachLabel function to split the
images into training and test data.
Read the category images and create image sets.
setDir = fullfile(toolboxdir('vision'),'visiondata','imageSets'); imds = imageDatastore(setDir,'IncludeSubfolders',true,'LabelSource',... 'foldernames');
Separate the sets into training and test image subsets. In this example, 30% of the images are partitioned for training and the remainder for testing.
[trainingSet,testSet] = splitEachLabel(imds,0.3,'randomize');
Create a visual vocabulary, or bag of features, by extracting feature descriptors from representative images of each category.
defines the features, or visual words, by using the k-means
clustering (Statistics and Machine Learning Toolbox) algorithm on the feature descriptors extracted
trainingSets. The algorithm iteratively
groups the descriptors into k mutually exclusive
clusters. The resulting clusters are compact and separated by similar
characteristics. Each cluster center represents a feature, or visual
You can extract features based on a feature detector, or you
can define a grid to extract feature descriptors. The grid method
may lose fine-grained scale information. Therefore, use the grid for
images that do not contain distinct features, such as an image containing
scenery, like the beach. Using speeded up robust features (or SURF)
detector provides greater scale invariance. By default, the algorithm
This algorithm workflow analyzes images in their entirety. Images must have appropriate labels describing the class that they represent. For example, a set of car images could be labeled cars. The workflow does not rely on spatial information nor on marking the particular objects in an image. The bag-of-visual-words technique relies on detection without localization.
returns an image classifier. The function trains a multiclass classifier
using the error-correcting output codes (ECOC) framework with binary
support vector machine (SVM) classifiers. The
uses the bag of visual words returned by the
bagOfFeatures object to encode
images in the image set into the histogram of visual words. The histogram
of visual words are then used as the positive and negative samples
to train the classifier.
to encode each image from the training set. This function detects
and extracts features from the image and then uses the approximate
nearest neighbor algorithm to construct a feature histogram for each
image. The function then increments histogram bins based on the proximity
of the descriptor to a particular cluster center. The histogram length
corresponds to the number of visual words that the
bagOfFeatures object constructed.
The histogram becomes a feature vector for the image.
Repeat step 1 for each image in the training set to create the training data.
Evaluate the quality of the classifier. Use the
to test the classifier against the validation image set. The output
confusion matrix represents the analysis of the prediction. A perfect
classification results in a normalized matrix containing 1s on the
diagonal. An incorrect classification results fractional values.
on a new image to determine its category.
 Csurka, G., C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual Categorization with Bags of Keypoints. Workshop on Statistical Learning in Computer Vision. ECCV 1 (1–22), 1–2.