deep learning at a high level provides you with the unique ability to train models to classify
data by feeding the algorithm ground truth ground truth refers to information provided by empirical
evidence or observation in artificial intelligence ground truth refers to datasets with labels to
indicate a classification characteristic for your data of interest for all you Silicon Valley fans
out there do you remember Ginni Ang's hot dog not hot dog app if we were building an application to
detect hot dogs our ground truth would be a labeled dataset of hot dog universe labeling
can be used for different data artifacts like text audio signals images videos etc now you can
appreciate that with the largest amount of data that deep learning applications require labeling
data can be a very labor-intensive process MATLAB provides you with several tools to help automate
this laborious process namely the image labeler for images video label and come to the labels for
videos or image sequences and the signal or audio label for digital and audio signals let's see how
we can use the video labeler app to label a video now remember although we will only show you the
video label rap in this video the same principles can be applied when using the other labeling tools
as well you can open up the video label app from the app stab or by typing in video labeler into
the command prompt once the app is launched click on the load button to load in the data source in
our case this would be a video file you can use the playback icons on the bottom to preview the
data let's assume that we need to label all the big red buoys in this video click the label icon
to create a new label and let's give it a name labels can be of three types a rectangular line
and pixel labels one caveat is that only rectangle labels are supported by the built-in automation
algorithms the next step is to mark the ROI in the frame to do this let's go back to the start
of the frame select the label and right click and drag around the area of interest we can now
employ one of the built-in automation algorithms or add your own custom automation algorithm to
automatically label the other frames in the video in this video we will take a look at the built-in
algorithms but stay tuned for another video on how to use your own custom algorithm so first
let's go and select a subset of the data that we need to label now let's select the label and
choose an automation algorithm say point tracker clicking on the automation button will take us
to the automation pane which provides you with information on how to use this algorithm on the
right hand side you can change the settings of the point tracker algorithm to use a different feature
detector let's choose the MSC our feature detector to learn more about these feature detectors please
take a look at the documentation now clicking on the Run button will automate the process
once complete you can use the playback icons on the bottom to verify the labels and from
the looks of it this algorithm did not work too well for our application let's hit the
cancel button to delete all the labels that we've created and let's choose the temporal
interpolator algorithm instead the temporal interpolated algorithm requires us to label
at least two key frames of interest that the algorithm will interpolate the location of the
bounding box between these two frames so let's go ahead and label a few frames of interest
now once you've labeled a few frames let's hit run we see that the algorithm is able to better
track the Bui now that we are satisfied with the results let's click the accept button now
you can see that we have a few labeled images you can now save this session and return to it
to continue labeling at a later time another kind of label that can be used or seen labels
see labels can be used to label entire frames or a range of frames rather than just region of
interest now that you have a label dataset you can export this data out of MATLAB as a ground
truth data object which contains information like the data source the label definitions and bounding
box locations of the regions of interest this ground truth object can be used to train machine
learning and deep learning algorithms in MATLAB click on the links in the description to learn
how to do this to conclude you can use tools like the video labeler Image labeler and the
ground truth label to label images and video frames that can be used to train and evaluate
object detectors and classifiers you can also apply the same principles to label signal and
audio data using the signal or audio label on the app check out the links below to learn more