Artificial Intelligence(AI)

Image Classification vs Pattern Recognition vs Object Detection vs Object Tracking–A Primer

December 24, 2018 Algorithms, Artificial Intelligence(AI), Computer Vision, Emerging Technologies, Image Classification, Image Recognition, Object Based Image Analysis, Object Classification, Object Detection, Object Recognition, Object Tracking, Pixel Based Image Analysis No comments

It is a common question that has been asked in all Artificial Intelligence Conference or Discussion Forums. Based on my knowledge, I thought of answering some of these questions:

1.) Image Classification (also called Image Recognition): is the process of creating a thematic image where each pixel is assigned a number representing a class / tag (this also includes ‘unclassified’. In a persons image the classes can be “complexion”, “gender”, “ethnicity”, “representational names”, etc.  

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2.) Pattern Recognition (also called Object Recognition):  Identifying/recognising the things or objects in an image. This answers the query – what objects or things are depicted in this image? For example, if you are searching for farm lands in an areal image, and other objects such as tractors, sheep’s, cattle’s etc.  There are two classifiers you can familiarize for pattern recognition.  Pixel based classification and object based classification.

  • Pixel based classification: Most of the image analysis algorithms you would encounter would be based on pixel based, However, there is a major drawback for pixel based algorithms that they do not take contextual information in to consideration. Context is how these pixel/object is associated to its environment(a relationship).
  • Object based classification: To overcome this limitation of pixel based classification, we could use Object based image analysis(OBIA) to divide images in to meaningful image-objects and evaluating their characteristics in spatial, spectral and temporal aspects.

Here is how both classification will look like one vs the other:

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3.) Object Detection: is another confusing terminology, Object Recognition was able to recognize – what type of object it is? Now object detection answers the query – where is this specific object?  Object detection is based on the point of interest of any given image; for instance electronic devices such as laptop/mobile phone in picture and recognition talked about the specific information about electronic devices, like name, type and other characteristic of particular interest point.

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4.) Object Tracking: Done in motion pictures like in animated gifs or videos, we want to track how an object is moving, where is it going, or its speed. For example: vehicle tracking in a traffic camera system.Or a Realtime object or people tracking like in Object Detection figure.

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Azure Cognitive Services–Experience Image Recognition using Custom Vision (Build an Harrison Ford Classifier)

December 23, 2018 Algorithms, Artificial Intelligence(AI), Azure AI, Cognitive Services, Compuer Vision Service, Computer Vision API, Custom Vision API, Custom Vision Service, Emerging Technologies, Machine Learning(ML) No comments

Custom Vision Service as part of Azure Cognitive Services landscape of pretrained API services, provides you an ability to customize the state-of-the-art Computer Vision models for your specific use case.

Using custom vision service you can upload set of images of your choice and categorize them accordingly using tags/categories and automatically train the image recognition classifiers to learn from these images and come up with image recognition predictions when you supply an input image. Later consume this service as an API in your existing applications.

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For example:  Here is how an image of Hollywood Actor – Harrison ford being accurately predicted by the custom model through training using a series of pictures of Harrison Ford through different ages and shapes.

I build this sample during Global AI Bootcamp Letterkenny– Hands on Labs, and will take you further through this article. Harrison Ford is my all time favourite actor.

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Another example, Harrison Ford was one among 3 in a photo. Here is how the results would look like.

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Here is how Harrison Ford’ sons picture is being predicted as Harrison Ford, due to similar facial characteristics. |f we further train this model, we can improve it’s capabilities to come up with accurate predictions.

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Now let us see, how it was implemented.

In this article I am going to use a set of Harrison Ford images found on Google Images and then upload them to Custom Vision service like below. For more accuracy, I tried to collect images of Harrison Ford through different stages of his life, so that computer vision model could evolve to predict more accurate results.

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Getting Started with Custom Vision:

The Azure Custom Vision API is a cognitive service that lets you build, deploy and improve custom image classifiers. An image classifier is an AI service that sorts images into classes (tags) according to certain characteristics. Unlike the Computer Vision service, Custom Vision allows you to create your own classifications. The Custom Vision service uses a machine learning algorithm to classify images.

Classification and object detection

Custom Vision functionality can be divided into two features. Image classification assigns a distribution of classifications to each image. Object detection is similar, but it also returns the coordinates in the image where the applied tags can be found.

To get started with our example,  first you need to have a Microsoft Account and Register/Login to https://www.customvision.ai

There going to be five steps of activities we are going to do:

1. Setup a Custom Vision Project

Create a new Project by selecting ‘New Project’ button

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Specify the naming as the followed:

  • Name: HarrisonFordClassifier
  • Description: HarrisonFordClassifier
  • Resource Group: Leave it default to ‘Limited Trial’
  • Project Types: Classification
  • Classification Types: Multi Label (this is essential, we are going to add multiple tags per image in this example: for say ‘Actor’, ‘Person’ and ‘Harrison Ford’
  • Domain: General (for now)

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2. Upload the Images

a.) Prepare Images

I have gathered a set of images you can download it from here, and extract the HF-Demo-Images.zip in to a folder of your choice.

There are two folders in it  first folder(harrisonford) contains all reference images for training the model and second folder(hf-quicktest) contains all the quick test images we are going to use for evaluating the model.

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b.) Create Tags

Select ‘+’ icon to create a new tag and create the following tags

  • Actor
  • Hollywood
  • Harrison Ford
  • Person
  • Male

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Enter Tag Name and click on ‘Save

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c.) Upload Images

Now that we created all the tags, lets upload the images and tag them with respective tags.

Click on ‘Add Images’ button and select the images from “harrisonford” folder to upload.

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d.) Assign Tags

Now specify the associated tags in My Tags section, selecting from the drop down

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Then click on Upload

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Have a review of the images uploaded

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3. Train

Now let us train the model by selecting the green train button on top right hand side of the page

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This initiates the first automatic training(Iteration 1) based on the tags you assigned and images associated to it.

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Once that step is completed let us review the output of the training.

It shows a precision and Recall of 100% indicates our image classification model is trained now to provide Precision of 100% and Recall of 100%.

PS: Recall means out of the tags which should be predicted correctly, what percentage did our model correctly find?

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4. Evaluate the Model

Now that our classifier is trained, let us evaluate the accuracy. For that we are going to use the sample images from “hf-quicktest” folder.

a.) First click on Quick Test button on top – image

b.) Select a local image or select an image URL

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Lets try another image

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Next let us try to upload an image of Ben Ford (Harrison Ford’s son)

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5. Active Learning

Now that we have couple of accurate predictions, Active Learning involves training the model again from the prediction samples we used. This would make the model evolve to provide us more accurate predictions, for example we correcting the model as it identified that Ben Ford also as Harrison Ford based on similar facial features. In real world, he is a different entity other than his father.

Ben Ford is a Chef by profession. So I am going to upload some of his pictures and tag them as Ben Ford. Also couple of images of both father and son together, and then initiate the training again. Hope they would not feel agitated.

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Now if you look at training performance, Precision and Recall values came down a bit, we can realize it is because we have two persons being tagged with some common tags etc.

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Let us do a Quick Test with the previous image of Ben Ford again. voilà!, we have some accurate prediction.

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Similarly, we can repurpose some of the previous prediction images from Predictions tab and add them with right Tags. Then retrain the model again to evolve the model.

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The End:

Now that you have learned how you can train Custom Vision API with set of images and retrain them again for more accuracy. Once your training is completed and you are happy with the performance, you can integrate the logic in to your existing apps using Custom Vision REST APIs. You can follow the HOL that covers the integration topic here.

Custom Vision Services provides you state-of-the-art Classification and object detection capabilities to customize it for your specific need with quick and easy steps. This help you reduce your time to market and increase ROI (Return of Investment) for your product lines or ideas.

Start learning today using the below reference links.

References:

Disclaimer: All the images referenced in this article are available on the public domain and there is no way any private images are been included in this examples. We respect Harrison Ford and his family privacy, this article is just an attempt to prove the capabilities of Azure Custom Vision Services, no way intended to insult or invade Mr.Harrison Ford’s privacy.I am a big fan of you sir.

Introducing Azure IoT Edge

May 13, 2017 .NET, Analytics, Artificial Intelligence(AI), Augmented Reality, Azure, Azure IoT Suite, Cloud Computing, Data Analytics, Edge Analytics, Embedded, Emerging Technologies, Event Hubs, Industrial Automation, Intelligent Cloud, Intelligent Edge, IoT, IoT Edge, IoT Hub, Linux, Mac OSX, Machine Learning(ML), Microsoft, Robotics, Self Driven Cars, Stream Analytics, Windows, Windowz Azure No comments

During Build! 2017 Microsoft has announced the availability of Azure IoT Edge, which would bring in some of the cloud capabilities to edge devices/networks within your Enterprise. This would enable industrial devices to utilize the capabilities of IoT in Azure within their constrained resources . 

With this Microsoft now makes it easier for developers to move some of their computing needs to these devices.  Edge devices are mostly having small foot print based to high end machines within your company network.

The essential capabilities to be supported by Azure IoT edge  include:

  • Perform Edge Analytics (a cut down version of Azure Stream Analytics)- Instead of doing analytics in cloud developer/implementer can move the basic cloud data processing and analytical capabilities to Edge Device. Run your machine learning algorithms in Edge device and take predictive analytics steps.
  • Perform Artificial Intelligence processing at edge device itself. Availability of Microsoft Cognitive Service on edge device would bring in whole lot of automation capabilities. Imagine Alexa/Siri working without internet connection, it should be able to provide you reminders etc.
  • Perform RealTime Decision making locally based on predefined rules.
  • Reduce bandwidth costs
  • Connect to other Edge devices and legacy devices within the constrained/corporate network.
  • Deploy IoT solutions to Edge Device from Cloud and provide updates as needed.
  • Operate offline without the need of real-time internet connectivity or intermittent connectivity. Doesn’t have to rely on Cloud to provide commands for processing, can do offline data capture and processing of information from other devices connected and take decisions without the need to rely on a connected cloud service.

Azure IoT Edge enables seamless deployment of cloud services such as:

Along with sharing the image represents Azure’s Enterprise Digital Vision, we will discuss about the same in later sessions:

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Getting Started & More information: