imageCategoryClassifier

Predict image category

Description

The imageCategoryClassifier object contains a linear support vector machine (SVM) classifier trained to recognize an image category.

You must have a Statistics and Machine Learning Toolbox™ license to use this classifier. This classifier supports parallel computing using multiple MATLAB^® workers. Enable parallel computing using the Computer Vision Toolbox Preferences dialog. To open the Computer Vision Toolbox™ preferences, on the Home tab, in the Environment section, click Preferences. Select Computer Vision System Toolbox.

Creation

Use the trainImageCategoryClassifier function to create the imageCategoryClassifier object.

bag = bagOfFeatures(trainingSet);
categoryClassifier = trainImageCategoryClassifier(trainingSet,bag);

Properties

Labels — Category labels

cell array

Category labels, specified as a cell array.

NumCategories — Number of trained categories

integer

Number of trained categories, stored as an integer value.

Object Functions

evaluate	Evaluate image classifier on collection of image sets
predict	Predict image category

Examples

Train, Evaluate, and Apply Image Category Classifier

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.

* Creating a 500 word visual vocabulary.
* Number of levels: 1
* Branching factor: 500
* Number of clustering steps: 1

* [Step 1/1] Clustering vocabulary level 1.
* Number of features          : 61440
* Number of clusters          : 500
* Initializing cluster centers...100.00%.
* Clustering...completed 55/100 iterations (~0.61 seconds/iteration)...converged in 55 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 = 2×2

    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);

Encoding images using Bag-Of-Features.
--------------------------------------
* Encoding an image...done.

Display the classification label.

categoryClassifier.Labels(labelIdx)

ans = 1x1 cell array
    {'cups'}

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.

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set 'UseParallel' to true or enable this by default using the Computer Vision Toolbox preferences.

For more information, see Parallel Computing Toolbox Support.

Version History

Introduced in R2014b