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imageCategoryClassifier class

Predict image category

Syntax

imageCategoryClassifier

Description

example

imageCategoryClassifier is returned by the trainImageCategoryClassifier function. It contains a linear support vector machine (SVM) classifer 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 System Toolbox Preferences dialog. To open the Computer Vision System Toolbox™ preferences, on the Home tab, in the Environment section, click Preferences. Select Computer Vision System Toolbox.

Properties

expand all

Category labels, specified as a cell array.

Number of trained categories, stored as an integer value.

Methods

evaluateEvaluate image classifier on collection of image sets
predictPredict image category

Examples

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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'}

References

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