imageLIME
Explain image classification result using LIME
Syntax
Description
scoreMap = imageLIME(net,X,label)X when the network
net evaluates the class score for the class given by
label. Use this function to explain classification decisions and
check that your network is focusing on the appropriate features of the image.
The LIME technique approximates the classification behavior of the
net using a simpler, more interpretable model. By generating
synthetic data from input X, classifying the synthetic data using
net, and then using the results to fit a simple regression model, the
imageLIME function determines the importance of each feature of
X to the network's classification score for class given by
label.
This function requires Statistics and Machine Learning Toolbox™.
[
also returns a map of the features used to compute the LIME results and the calculated
importance of each feature.scoreMap,featureMap,featureImportance] = imageLIME(net,X,label)
___ = imageLIME(___,
specifies options using one or more name-value pair arguments in addition to the input
arguments in previous syntaxes. For example, Name,Value)'NumFeatures',100 sets the
target number of features to 100.
Examples
Visualize Which Parts of an Image are Important for Classification
Use imageLIME to visualize the parts of an image are important to a network for a classification decision.
Import the pretrained network SqueezeNet.
net = squeezenet;
Import the image and resize to match the input size for the network.
X = imread("laika_grass.jpg");
inputSize = net.Layers(1).InputSize(1:2);
X = imresize(X,inputSize);Display the image. The image is of a dog named Laika.
imshow(X)
Classify the image to get the class label.
label = classify(net,X)
label = categorical
toy poodle
Use imageLIME to determine which parts of the image are important to the classification result.
scoreMap = imageLIME(net,X,label);
Plot the result over the original image with transparency to see which areas of the image affect the classification score.
figure imshow(X) hold on imagesc(scoreMap,'AlphaData',0.5) colormap jet
The network focuses predominantly on Laika's head and back to make the classification decision. Laika's eye and ear are also important to the classification result.
Visualize Only the Most Important Features
Use imageLIME to determine the most important features in an image and isolate them from the unimportant features.
Import the pretrained network SqueezeNet.
net = squeezenet;
Import the image and resize to match the input size for the network.
X = imread("sherlock.jpg");
inputSize = net.Layers(1).InputSize(1:2);
X = imresize(X,inputSize);Classify the image to get the class label.
label = classify(net,X)
label = categorical
golden retriever
Compute the map of the feature importance and also obtain the map of the features and the feature importance. Set the image segmentation method to 'grid', the number of features to 64, and the number of synthetic images to 3072.
[scoreMap,featureMap,featureImportance] = imageLIME(net,X,label,'Segmentation','grid','NumFeatures',64,'NumSamples',3072);
Plot the result over the original image with transparency to see which areas of the image affect the classification score.
figure imshow(X) hold on imagesc(scoreMap,'AlphaData',0.5) colormap jet colorbar
Use the feature importance to find the indices of the most important five features.
numTopFeatures = 5; [~,idx] = maxk(featureImportance,numTopFeatures);
Use the map of the features to mask out the image so only the most important five features are visible. Display the masked image.
mask = ismember(featureMap,idx); maskedImg = uint8(mask).*X; figure imshow(maskedImg);
