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I've learnt about how to extract features from a single image as described in this example: https://www.mathworks.com/help/vision/ref/extractlbpfeatures.html

Now I am working with datasets of 1000 images for my matlab project to extract features of bicycle, car and motorbike. I have three separate folders including bicycle, car and motorbike in my dataset. During execution, I am getting error saying,

Error using extractLBPFeatures>parseInputs (line 148)
Expected I to be one of these types:
double, single, int16, uint16, uint8, logical
Instead its type was imageSet.
Error in extractLBPFeatures (line 129)
params = parseInputs(I,varargin{:});
Error in LBP (line 21)
bycycleLBP = extractLBPFeatures(bycycleData,'Upright',false);
What should I do? Below is my sample code ==>
imSet = imageSet('dataset\train','recursive');
bicycleData = imSet(1);
carData = imSet(2);
motorbikeData = imSet(3);
%%Extract LBP Features
bicycleLBP = extractLBPFeatures(bicycleData,'Upright',false);
carLBP = extractLBPFeatures(carData,'Upright',false);
motorbikeLBP = extractLBPFeatures(motorbikeData,'Upright',false);
bicycle = bicycleLBP.^2;
car = carLBP.^2;
motorbike = motorbikeLBP.^2;
figure
bar([bicycle; car; motorbike]','grouped');
title('LBP Features Of bicycle, car and motorbike');
xlabel('LBP Histogram Bins');
legend('Bicycle','Car','Motorbike');
Please help me with my sample code implemented.
Let's look at two variables before you attempt to extract the features.
>> whos imSet bicycleData
  Name             Size            Bytes  Class       Attributes            
  imSet            1x3              1494  imageSet 
  bicycleData      1x1               498  imageSet 
The variable imSet is a list of 3 imageSet objects. The first represents bicycles, so you properly pull the bicycle imageSet into its own variable bicycleData, which is a singular imageSet. So far so good, but when we look at the documentation for extractLBPFeatures...
features = extractLBPFeatures(I,Name,Value)
I — Input image
Input image, specified as an M-by-N 2-D grayscale image that is real, and non-sparse.
This function can only extract the features of one grayscale image at a time. You'll have to iterate through your imageSet to extract the features one at a time.
% Create a cell array to store features per image.
bicycleFeatures = cell(size(bicycleData.ImageLocation));
for i = 1:length(bicycleFeatures)
    % Read in individual image, and convert to grayscale to extract features.
    image = imread(bicycleData.ImageLocation{i});
    bicycleFeatures{i} = extractLBPFeatures(rgb2gray(image));
Keep in mind that you'll still have post-processing work to do. This extracts the features of each image, so you'll have to determine how you want to combine the feature data across each data set.
        
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