Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

trainImageCategoryClassifier

Train an image category classifier

Syntax

classifier = trainImageCategoryClassifier(imds,bag)
classifier = trainImageCategoryClassifier(imds,bag,Name,Value)

Description

example

classifier = trainImageCategoryClassifier(imds,bag) returns an image category classifier. The classifier contains the number of categories and the category labels for the input imds images. The function trains a support vector machine (SVM) multiclass classifier using the input bag, a bagOfFeatures object.

You must have a Statistics and Machine Learning Toolbox™ license to use this function.

This function supports parallel computing using multiple MATLAB® workers. Enable parallel computing using the Computer Vision System Toolbox Preferences dialog. To open Computer Vision System Toolbox™ preferences, on the Home tab, in the Environment section, click Preferences. Select Computer Vision System Toolbox.

classifier = trainImageCategoryClassifier(imds,bag,Name,Value) returns a classifier object with optional input properties specified by one or more Name,Value pair arguments.

Examples

collapse all

Load two image categories.

setDir  = fullfile(toolboxdir('vision'),'visiondata','imageSets');
imds = imageDatastore(setDir,'IncludeSubfolders',true,'LabelSource',...
    'foldernames');

Split the data set into a training and test data. Pick 30% of images from each set for the training data and the remainder 70% for the test data.

[trainingSet,testSet] = splitEachLabel(imds,0.3,'randomize');

Create bag of visual words.

bag = bagOfFeatures(trainingSet);
Creating Bag-Of-Features.
-------------------------
* Image category 1: books
* Image category 2: cups
* Selecting feature point locations using the Grid method.
* Extracting SURF features from the selected feature point locations.
** The GridStep is [8 8] and the BlockWidth is [32 64 96 128].

* Extracting features from 4 images...done. Extracted 76800 features.

* Keeping 80 percent of the strongest features from each category.

* Using K-Means clustering to create a 500 word visual vocabulary.
* Number of features          : 61440
* Number of clusters (K)      : 500

* Initializing cluster centers...100.00%.
* Clustering...completed 21/100 iterations (~1.02 seconds/iteration)...converged in 21 iterations.

* Finished creating Bag-Of-Features

Train a classifier with the training sets.

categoryClassifier = trainImageCategoryClassifier(trainingSet,bag);
Training an image category classifier for 2 categories.
--------------------------------------------------------
* Category 1: books
* Category 2: cups

* Encoding features for 4 images...done.

* Finished training the category classifier. Use evaluate to test the classifier on a test set.

Evaluate the classifier using test images. Display the confusion matrix.

confMatrix = evaluate(categoryClassifier,testSet)
Evaluating image category classifier for 2 categories.
-------------------------------------------------------

* Category 1: books
* Category 2: cups

* Evaluating 8 images...done.

* Finished evaluating all the test sets.

* The confusion matrix for this test set is:


             PREDICTED
KNOWN    | books   cups   
--------------------------
books    | 0.75    0.25   
cups     | 0.25    0.75   

* Average Accuracy is 0.75.


confMatrix =

    0.7500    0.2500
    0.2500    0.7500

Find the average accuracy of the classification.

mean(diag(confMatrix))
ans =

    0.7500

Apply the newly trained classifier to categorize new images.

img = imread(fullfile(setDir,'cups','bigMug.jpg'));
[labelIdx, score] = predict(categoryClassifier,img);

Display the classification label.

categoryClassifier.Labels(labelIdx)
ans =

  1x1 cell array

    {'cups'}

Input Arguments

collapse all

Images specified as an imageDatastore object.

Bag of features, specified as a bagOfFeatures object. The object contains a visual vocabulary of extracted feature descriptors from representative images of each image category.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'Verbose',true sets 'Verbose' to the logical true.

collapse all

Enable progress display to screen, specified as the comma-separated pair consisting of 'Verbose' and the logical true or false.

Classifier options, specified as the comma-separated pair consisting of 'LearnerOptions' and the learner options output returned by the templateSVM function.

Example 2. Example

To adjust the regularization parameter of templateSVM and to set a custom kernel function, use the following syntax:

opts = templateSVM('BoxConstraint',1.1,'KernelFunction','gaussian');
classifier = trainImageCategoryClassifier(imds,bag,'LearnerOptions',opts);

Output Arguments

collapse all

Image category classifier, returned as an imageCategoryClassifier object. The function trains a support vector machine (SVM) multiclass classifier using the error correcting output codes (ECOC) framework.

References

[1] Csurka, G., C. R. Dance, L. Fan, J. Willamowski, and C. Bray Visual Categorization with Bag of Keypoints, Workshop on Statistical Learning in Computer Vision, ECCV 1 (1-22), 1-2.

Introduced in R2014b