Step 1: Set Up Image Category Sets

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');

Step 2: Create Bag of Features

Create a visual vocabulary, or bag of features, by extracting feature descriptors from representative images of each category.

The bagOfFeatures object defines the features, or visual words, by using the k-means clustering (Statistics and Machine Learning Toolbox) algorithm on the feature descriptors extracted from 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 word.

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 runs the 'grid' method.

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.

Step 3: Train an Image Classifier With Bag of Visual Words

The trainImageCategoryClassifier function 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 trainImageCategoryClassfier function 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.

Step 4: Classify an Image or Image Set

Use the imageCategoryClassifier predict method on a new image to determine its category.