Main Content


Balance image blocks using bounding boxes and big images



locationSet = balanceBoxLabels(boxLabels,bigLabeledImages,levels,blockSize,numObservations) balances bounding box labels that are contained in the big images object bigImages. The function returns locationSet, a blockLocationSet object that contains numObservations number of block locations, each of size blockSize.

locationSet = balanceBoxLabels(___,Name,Value) specifies options using one or more name-value pair arguments in addition to the input arguments from the previous syntax.


collapse all

Load box labels data that contains boxes and labels for one image. The height and width of each box is [20,20].

d  = load('balanceBoxLabelsData.mat');

bboxes    = d.BoxLabels.Boxes;
labels    = d.BoxLabels.Labels;
boxLabels = table(bboxes,labels);

Find the class imbalance in the box labels.

blds = boxLabelDatastore(boxLabels);
tbl1 = countEachLabel(blds);
h1 = histogram('Categories',tbl1.Label,'BinCounts',tbl1.Count);

Find the class imbalance by evaluating if the coefficient of variation is greater than 1.

CVBefore = std(tbl1.Count)/mean(tbl1.Count)
CVBefore = 1.5746

Set the number of observations by finding the median of the count of each class, and multiplying it by the number of classes.

numClasses = height(tbl1);
numObservations = mean(tbl1.Count)*numClasses;

Create a big image of size 500-by-500 pixels.

bigImages = bigimage(zeros([500,500]));

Set the image size of each observation.

blockSize = [50,50];

Set the resolution levels for the big image objects.

levels = 1;

Balance the box labels.

locationSet = balanceBoxLabels(boxLabels,bigImages,levels,blockSize,numObservations);
Balancing box labels for 1 images with [==================================================] 100%
[==================================================] 100%
Balancing box labels complete.

Count the labels that are contained within the image blocks.

bldsBalanced = boxLabelDatastore(boxLabels,locationSet);
tbl2 = countEachLabel(bldsBalanced);

Check if box labels are balanced. Compare new and original histograms of label count. If not balanced, use a different value for the number of blocks, numBlocks. The histograms show that the box labels are balanced.

hold on;
h2 = histogram('Categories',tbl2.Label,'BinCounts',tbl2.Count);
title(h2.Parent,'Balanced Class Labels');

Check if the coefficient of variation value is less than the original value.

CVAfter = std(tbl2.Count)/mean(tbl2.Count)
CVAfter = 0.3731

Input Arguments

collapse all

Labeled bounding box data, specified as a table with two columns.

  • The first column contains bounding boxes and must be a cell vector. Each element in the cell vector contains M-by-4 matrices in the format [x, y, width, height] for M boxes.

  • The second column must be a cell vector that contains the label names corresponding to each bounding box. Each element in the cell vector must be an M-by-1 categorical or string vector.

To create a box label table from ground truth data,

  1. Use the Image Labeler or Video Labeler app to label your ground truth. Export the labeled ground truth data to your workspace.

  2. Create a bounding box label datastore using the objectDetectorTrainingData function.

  3. You can obtain the boxLabels from the LabelData property of the box label datastore returned by objectDetectorTrainingData, ( blds.LabelData).

Labeled big images, specified as a bigimage object or vector of bigimage objects containing pixel label images.

Resolution levels of blocks from each big image in bigLabeledImages, specified as a positive integer scalar or a vector of positive integers that is equal to the length of the bigLabeledImages vector. If you specify a scalar value, then all big labeled images supply blocks at the same resolution level.

Data Types: double

Block size of read data, specified as a two-element row vector of positive integers, [numrows,numcols]. The first element specifies the number of rows in the block. The second element specifies the number of columns.

Number of block locations to return, specified as a positive integer.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'OverlapThreshold','1'

Overlap threshold, specified as the comma-separated pair consisting of 'OverlapThreshold' and a positive scalar in the range [0,1]. When the overlap between a bounding box and a cropping window is greater than the threshold, boxes in the boxLabels input are clipped to the image block window border. When the overlap is less than the threshold, the boxes are discarded. When you lower the threshold, part of an object can get discarded. To reduce the amount an object can be clipped at the border, increase the threshold. Increasing the threshold can also cause less-balanced box labels.

The amount of overlap between the bounding box and a cropping window is defined as.


Display progress information, specified as the comma-separated pair of 'Verbose' and a numeric or logical 1 (true) or 0 (false). Set this property to true to display information.

Output Arguments

collapse all

Balanced box labels, returned as a blockLocationSet object. The object contains numObservations number of locations of balanced blocks, each of size blockSize.


collapse all

Balancing Box Labels

To balance box labels, the function over samples classes that are less represented in the big image. The box labels are counted across the dataset and sorted based on each class count. Each image size is split into several quadrants, based on the blockSize input value. The algorithm randomly picks several blocks within each quadrant with less-represented classes. The blocks without any objects are discarded. The balancing stops once the specified number of blocks are selected.

Checking for Balance

You can check the success of balancing by comparing the histograms of label count before and after balancing. You can also check the coefficient of variation value. For best results, the value should be less than the original value. For more information, see the National Institute of Standards and Technology (NIST) website, see Coefficient of Variation for more information.

Introduced in R2020a