Custom feature extractor is not recognized by bagOfFeatures function

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Daigo
Daigo 2024-7-22
评论： Daigo 2024-7-23
Ran in:
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?

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采纳的回答

Walter Roberson
Walter Roberson 2024-7-23
Use bagOfFeatures with 'CustomExtractor' option.
Pass this bag as the second parameter of indexImages

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