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Custom feature extractor is not recognized by bagOfFeatures function

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Hi, I'm trying to create a bag-of-visual-words model using my custom feature extractor function, but the BoW is always generated using SURF features. Below is a toy example to reproduce the problem:
clc; clear all; close all;
% Load images in the workspace
imageDir = fullfile(toolboxdir('vision'),'visiondata','structureFromMotion');
% Create an imageDataStore object
imds = imageDatastore(imageDir);
% Test the feature extractor
I = readimage(imds, 1);
[features, featureMetrics] = exampleExtractor(I);
% Create a virtual vocabulary with the custom feature descriptor
extractor = @exampleExtractor;
imageIndex = indexImages(imds);
Creating an inverted image index using Bag-Of-Features. ------------------------------------------------------- Creating Bag-Of-Features. ------------------------- * Selecting feature point locations using the Detector method. * Extracting SURF features from the selected feature point locations. ** detectSURFFeatures is used to detect key points for feature extraction. * Extracting features from 5 images...done. Extracted 2980 features. * Keeping 80 percent of the strongest features from each category. * Balancing the number of features across all image categories to improve clustering. ** Image category 1 has the least number of strongest features: 2384. ** Using the strongest 2384 features from each of the other image categories. * Creating a 2384 word visual vocabulary. * Number of levels: 1 * Branching factor: 2384 * Number of clustering steps: 1 * [Step 1/1] Clustering vocabulary level 1. * Number of features : 2384 * Number of clusters : 2384 * Initializing cluster centers...100.00%. * Clustering...completed 1/100 iterations (~0.03 seconds/iteration)...converged in 1 iterations. * Finished creating Bag-Of-Features Encoding images using Bag-Of-Features. -------------------------------------- * Encoding 5 images...done. Finished creating the image index.
bag = bagOfFeatures(imds, 'CustomExtractor', extractor, ...
'TreeProperties', [2, 10], 'StrongestFeatures', 1);
Creating Bag-Of-Features. ------------------------- * Extracting features using a custom feature extraction function: exampleExtractor. * Extracting features from 5 images...done. Extracted 242966 features. * Keeping 100 percent of the strongest features from each category. * Creating a 100 word visual vocabulary. * Number of levels: 2 * Branching factor: 10 * Number of clustering steps: 11 * [Step 1/11] Clustering vocabulary level 1. * Number of features : 242966 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 44/100 iterations (~0.35 seconds/iteration)...converged in 44 iterations. * [Step 2/11] Clustering vocabulary level 2, branch 1. * Number of features : 42488 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 32/100 iterations (~0.05 seconds/iteration)...converged in 32 iterations. * [Step 3/11] Clustering vocabulary level 2, branch 2. * Number of features : 13566 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 33/100 iterations (~0.03 seconds/iteration)...converged in 33 iterations. * [Step 4/11] Clustering vocabulary level 2, branch 3. * Number of features : 26142 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 41/100 iterations (~0.04 seconds/iteration)...converged in 41 iterations. * [Step 5/11] Clustering vocabulary level 2, branch 4. * Number of features : 27133 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 31/100 iterations (~0.04 seconds/iteration)...converged in 31 iterations. * [Step 6/11] Clustering vocabulary level 2, branch 5. * Number of features : 24487 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 31/100 iterations (~0.04 seconds/iteration)...converged in 31 iterations. * [Step 7/11] Clustering vocabulary level 2, branch 6. * Number of features : 16289 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 20/100 iterations (~0.04 seconds/iteration)...converged in 20 iterations. * [Step 8/11] Clustering vocabulary level 2, branch 7. * Number of features : 18651 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 31/100 iterations (~0.05 seconds/iteration)...converged in 31 iterations. * [Step 9/11] Clustering vocabulary level 2, branch 8. * Number of features : 37195 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 27/100 iterations (~0.06 seconds/iteration)...converged in 27 iterations. * [Step 10/11] Clustering vocabulary level 2, branch 9. * Number of features : 14981 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 46/100 iterations (~0.04 seconds/iteration)...converged in 46 iterations. * [Step 11/11] Clustering vocabulary level 2, branch 10. * Number of features : 22034 * Number of clusters : 10 * Initializing cluster centers...100.00%. * Clustering...completed 37/100 iterations (~0.04 seconds/iteration)...converged in 37 iterations. * Finished creating Bag-Of-Features
function [features, featureMetrics, varargout] = exampleExtractor(I)
% Convert image to grayscale, if necessary
[height, width, numChannels] = size(I);
if numChannels > 1
grayImage = rgb2gray(I);
else
grayImage = I;
end
% Define a regular grid over I
gridStep = 8;
gridX = 1:gridStep:width;
gridY = 1:gridStep:height;
[X,Y] = meshgrid(gridX, gridY);
gridLocations = [X(:), Y(:)];
% Multi-scale feature extraction
multiscaleGridPoints = [SIFTPoints(gridLocations, 'Scale', 1.6);
SIFTPoints(gridLocations, 'Scale', 3.2);
SIFTPoints(gridLocations, 'Scale', 4.8)];
features = extractFeatures(grayImage, multiscaleGridPoints, ...
'Upright', true, 'Method', 'SIFT');
% Compute the feature metric
featureMetrics = var(features, [], 2);
% Optinal output
if nargout > 2
varargout{1} = multiscaleGridPoints.Location;
end
end
Although the custom feature extractor uses the SIFT feature in this code, the BoW is created using the SURF features:
Creating an inverted image index using Bag-Of-Features.
-------------------------------------------------------
Creating Bag-Of-Features.
-------------------------
* Selecting feature point locations using the Detector method.
* Extracting SURF features from the selected feature point locations.
** detectSURFFeatures is used to detect key points for feature extraction.
Does anyone know where I'm doing wrong?

Accepted Answer

Walter Roberson
Walter Roberson on 23 Jul 2024
Use bagOfFeatures with 'CustomExtractor' option.
Pass this bag as the second parameter of indexImages

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R2024a

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