Create a Custom Feature Extractor

You can use the bag-of-features (BoF) framework with many different types of image features. To use a custom feature extractor instead of the default speeded-up robust features (SURF) feature extractor, use theCustomExtractor财产的bagOfFeaturesobject.

Example of a Custom Feature Extractor

This example shows how to write a custom feature extractor function forbagOfFeatures. You can open this example function file and use it as a template by typing the following command at the MATLAB^®command prompt:

edit('exampleBagOfFeaturesExtractor.m')

Step 1. Define the image sets.
Step 2. Create a new extractor function file.
Step 3. Preprocess the image.
Step 4. Select a point location for feature extraction.
Step 5. Extract features.
Step 6. Compute the feature metric.

Define the set of images and labels

Read the category images and create image sets.

setDir = fullfile(toolboxdir('vision'),'visiondata','imageSets'); imds = imageDatastore(setDir,'IncludeSubfolders',true,'LabelSource',... 'foldernames');

创建一个新的器函数文件

The extractor function must be specified as a function handle:

extractorFcn = @exampleBagOfFeaturesExtractor; bag = bagOfFeatures(imgSets,'CustomExtractor',extractorFcn)

exampleBagOfFeaturesExtractoris a MATLAB function. For example:

function [features,featureMetrics] = exampleBagOfFeaturesExtractor(img) ...

You can also specify the optionallocationoutput:

function [features,featureMetrics,location] = exampleBagOfFeaturesExtractor(img) ...

The function must be on the path or in the current working folder.

Argument	Input/Output	Description
`img`	Input	Binary, grayscale, or truecolor image. The input image is from the image set that was originally passed into`bagOfFeatures`.
`features`	Output	A`binaryFeatures`object. AnM-by-Nnumeric matrix of image features, whereMis the number of features andNis the length of each feature vector. The feature length,N必须大于0,the same for all images processed during the`bagOfFeatures`creation process. If you cannot extract features from an image, supply an empty feature matrix and an empty feature metrics vector. Use the empty matrix and vector if, for example, you did not find any keypoints for feature extraction. Numeric, real, and nonsparse.
`featureMetrics`	Output	AnM-by-1 vector of feature metrics indicating the strength of each feature vector. Used to apply the`'SelectStrongest'`criteria in`bagOfFeatures`framework. Numeric, real, and nonsparse.
`location`	Output	AnM-by-2 matrix of 1-based [xy] values. The [xy] values can be fractional. Numeric, real, and nonsparse.

Preprocess the image

Input images can require preprocessing before feature extraction. To extract SURF features and to use thedetectSURFFeaturesordetectMSERFeaturesfunctions, the images must be grayscale. If the images are not grayscale, you can convert them using theim2grayfunction.

grayImage = im2gray(I);

Select a point location for feature extraction

Use a regular spaced grid of point locations. Using the grid over the image allows for dense SURF feature extraction. The grid step is in pixels.

gridStep = 8;gridX = 1:gridStep:width; gridY = 1:gridStep:height; [x,y] = meshgrid(gridX,gridY); gridLocations = [x(:) y(:)];

You can manually concatenate multipleSURFPointsobjects at different scales to achieve multiscale feature extraction.

multiscaleGridPoints = [SURFPoints(gridLocations,'Scale',1.6); SURFPoints(gridLocations,'Scale',3.2); SURFPoints(gridLocations,'Scale',4.8); SURFPoints(gridLocations,'Scale',6.4)];

Alternatively, you can use a feature detector, such asdetectSURFFeaturesordetectMSERFeatures, to select point locations.

multiscaleSURFPoints = detectSURFFeatures(I);

Extract features

Extract features from the selected point locations. By default,bagOfFeaturesextracts upright SURF features.

features = extractFeatures(grayImage,multiscaleGridPoints,'Upright',true);

Compute the feature metric

The feature metrics indicate the strength of each feature. Larger metric values are assigned to stronger features. Use feature metrics to identify and remove weak features before usingbagOfFeaturesto learn the visual vocabulary of an image set. Use the metric that is suitable for your feature vectors.

For example, you can use the variance of the SURF features as the feature metric.

featureMetrics = var(features,[],2);

If you used a feature detector for the point selection, then use the detection metric instead.

featureMetrics = multiscaleSURFPoints.Metric;

You can optionally return the feature location information. The feature location can be used for spatial or geometric verification image search applications. See theGeometric Verification Using estimateGeometricTransform2D Functionexample. TheretrieveImagesandindexImagesfunctions are used for content-based image retrieval systems.

if nargout > 2 varargout{1} = multiscaleGridPoints.Location; end