This document discusses building a personal image search engine using Amazon Rekognition. It begins by noting the explosive growth of personal photos. It then introduces Amazon Rekognition as a deep learning image recognition service that can detect objects, scenes, faces, and compare faces. The document outlines the APIs for object detection, facial analysis, face comparison, and facial recognition. It provides examples of how these APIs could be used to power photo tagging, targeted advertising, and facial verification. It then discusses using Rekognition to build a personal photo search engine based on TF-IDF (term frequency-inverse document frequency) modeling of image labels. Finally, it covers Amazon Rekognition pricing and availability.
Best Practices for Integrating Amazon Rekognition into Your Own ApplicationsAmazon Web Services
by Paul Roberts, Sr. Solutions Architect, AWS
Amazon Rekognition makes it easy to extract meaningful metadata from visual content. This session demonstrates practical approaches to enhance your media workflows with Amazon Rekognition, through common use cases. Demos and content will provide best practices for integrating Rekognition with other AWS services in real-world scenarios that help developers build image analysis quickly and confidently into their own applications.
An Overview of AI on the AWS Platform - February 2017 Online Tech TalksAmazon Web Services
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Learning Objectives
• Learn about the breadth of AI services available on the AWS Cloud
• Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition
• Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance
Best Practices for Integrating Amazon Rekognition into Your Own ApplicationsAmazon Web Services
by Paul Roberts, Sr. Solutions Architect, AWS
Amazon Rekognition makes it easy to extract meaningful metadata from visual content. This session demonstrates practical approaches to enhance your media workflows with Amazon Rekognition, through common use cases. Demos and content will provide best practices for integrating Rekognition with other AWS services in real-world scenarios that help developers build image analysis quickly and confidently into their own applications.
An Overview of AI on the AWS Platform - February 2017 Online Tech TalksAmazon Web Services
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Learning Objectives
• Learn about the breadth of AI services available on the AWS Cloud
• Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition
• Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance
NEW LAUNCH! Enhance Your Mobile Apps with AI Using Amazon LexAmazon Web Services
Amazon Echo and Alexa have shown that voice interfaces provide significant benefits to users – interactions are easy, fast, and context-driven. In this hands-on session, you’ll see how to add compelling voice and chat interfaces to your mobile apps, using Amazon Lex for processing conversations and triggering corresponding actions in your backend systems, all without having to manage any infrastructure. You’ll leave knowing how to build apps that can “Find me a nearby hotel” or “Reorder supplies for the copier”.
AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)Amazon Web Services
For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.
Artificial Intelligence in Fashion, Beauty and related Creative industriesPetteriTeikariPhD
Quick introduction for artificial intelligence / deep learning applications in fashion, beauty and creative industries.
Alternative download link: https://dl.dropboxusercontent.com/u/6757026/slideShare/creativeAI.pdf
Implications for computation aesthetics, art market prediction and neuroaesthetics
...
Computational analysis, art market price modeling and generative modeling for visual arts with multidisciplinary approach consisting of neuroaesthetics, computational aesthetics, quant trading and deep learning.
Alternative download link: https://www.dropbox.com/s/gtass3pl7t5metx/visualArtsPredictionSystem.pdf?dl=0
Motivational overview for why the medical image analysis need a volumetric equivalent of popular ImageNet database used in benchmarking deep learning architectures, and as a basis for transfer learning when not enough data is available for training the deep learning from scratch
Building a Machine Learning App with AWS LambdaSri Ambati
Ludi Rehaks' meetup on 03.17.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
Announcing Amazon Lex - January 2017 AWS Online Tech TalksAmazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.
Learning Objectives:
• Learn about the capabilities and features of Amazon Lex
• Learn about the benefits of Amazon Lex
• Learn about the different use cases
• Learn how to get started using Amazon Lex
Introducing Amazon Lex – A Service for Building Voice or Text Chatbots - Marc...Amazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.
Learning Objectives:
• Learn about the capabilities and features of Amazon Lex
• Learn about the benefits of Amazon Lex
• Learn about the different use cases
• Learn how to get started using Amazon Lex
Optimizing the Data Tier for Serverless Web Applications - March 2017 Online ...Amazon Web Services
AWS Lambda empowers developers to build cloud-native web applications or platforms using microservices architectures. This tech talk walks you through the process of identifying the presentation, logic, and data tiers required to build web applications with AWS Lambda at the core. By using AWS Lambda as your logic tier, you have a wide number of data storage options for your data tier. AWS offers a wide range of database services to fit your application requirements. We dive into methodologies for picking the right database/datastore technology based on your application requirements. We demonstrate connecting your serverless app to various AWS database offerings including Amazon RDS, Amazon Aurora, Amazon DynamoDB, and Amazon ElastiCache, and elaborate on the setup of each option with AWS Lambda. We also provide guidelines and best practices for implementing this architecture pattern, such as setting up a VPC on Lambda to connect to private resources and managing database connections.
Learning Objectives:
- Understand data-tier options when building serverless applications using AWS Lambda.
- Configuration and connectivity of AWS Lambda with each data tier
- Best practices for database connections and retries
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Deep learning-based image recognition: Intro to Amazon RekognitionAmazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos. Join this session and learn more about Amazon Rekognition!
Intro to Amazon Lightsail and Launching Your First Application on Amazon Ligh...Amazon Web Services
Amazon Lightsail is a simple cloud platform, designed to make running applications on AWS easy. Lightsail allows you to launch a virtual server with a few clicks and manage your infrastructure and applications from its intuitive interface. As your application grows, you can scale up easily with load balancers, attached block storage, and connections to other AWS services. With in-browser SSH and RDP access, easy server management, and in-console guidance, Lightsail provides all the tools needed for builders of all levels – no prior AWS experience required
This demo-focused session will walk you through the steps to launch an application on Lightsail and set up the framework used for the afternoon’s workshop. The session will also provide a brief overview of Amazon Rekogniton, AWS’s AI-driven image recognition service, which will help provide the ‘brains’ behind our workshop’s application.
Deep learning-based image recognition: Intro to Amazon Rekognition: Amazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos.
Deep Learning-based Image Recognition: Intro to Amazon RekognitionAmazon Web Services
by Mikhail Prudnikov, Solutions Architect, AWS
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos. Join this session and learn more about Amazon Rekognition!
NEW LAUNCH! Enhance Your Mobile Apps with AI Using Amazon LexAmazon Web Services
Amazon Echo and Alexa have shown that voice interfaces provide significant benefits to users – interactions are easy, fast, and context-driven. In this hands-on session, you’ll see how to add compelling voice and chat interfaces to your mobile apps, using Amazon Lex for processing conversations and triggering corresponding actions in your backend systems, all without having to manage any infrastructure. You’ll leave knowing how to build apps that can “Find me a nearby hotel” or “Reorder supplies for the copier”.
AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)Amazon Web Services
For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.
Artificial Intelligence in Fashion, Beauty and related Creative industriesPetteriTeikariPhD
Quick introduction for artificial intelligence / deep learning applications in fashion, beauty and creative industries.
Alternative download link: https://dl.dropboxusercontent.com/u/6757026/slideShare/creativeAI.pdf
Implications for computation aesthetics, art market prediction and neuroaesthetics
...
Computational analysis, art market price modeling and generative modeling for visual arts with multidisciplinary approach consisting of neuroaesthetics, computational aesthetics, quant trading and deep learning.
Alternative download link: https://www.dropbox.com/s/gtass3pl7t5metx/visualArtsPredictionSystem.pdf?dl=0
Motivational overview for why the medical image analysis need a volumetric equivalent of popular ImageNet database used in benchmarking deep learning architectures, and as a basis for transfer learning when not enough data is available for training the deep learning from scratch
Building a Machine Learning App with AWS LambdaSri Ambati
Ludi Rehaks' meetup on 03.17.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
Announcing Amazon Lex - January 2017 AWS Online Tech TalksAmazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.
Learning Objectives:
• Learn about the capabilities and features of Amazon Lex
• Learn about the benefits of Amazon Lex
• Learn about the different use cases
• Learn how to get started using Amazon Lex
Introducing Amazon Lex – A Service for Building Voice or Text Chatbots - Marc...Amazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.
Learning Objectives:
• Learn about the capabilities and features of Amazon Lex
• Learn about the benefits of Amazon Lex
• Learn about the different use cases
• Learn how to get started using Amazon Lex
Optimizing the Data Tier for Serverless Web Applications - March 2017 Online ...Amazon Web Services
AWS Lambda empowers developers to build cloud-native web applications or platforms using microservices architectures. This tech talk walks you through the process of identifying the presentation, logic, and data tiers required to build web applications with AWS Lambda at the core. By using AWS Lambda as your logic tier, you have a wide number of data storage options for your data tier. AWS offers a wide range of database services to fit your application requirements. We dive into methodologies for picking the right database/datastore technology based on your application requirements. We demonstrate connecting your serverless app to various AWS database offerings including Amazon RDS, Amazon Aurora, Amazon DynamoDB, and Amazon ElastiCache, and elaborate on the setup of each option with AWS Lambda. We also provide guidelines and best practices for implementing this architecture pattern, such as setting up a VPC on Lambda to connect to private resources and managing database connections.
Learning Objectives:
- Understand data-tier options when building serverless applications using AWS Lambda.
- Configuration and connectivity of AWS Lambda with each data tier
- Best practices for database connections and retries
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Deep learning-based image recognition: Intro to Amazon RekognitionAmazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos. Join this session and learn more about Amazon Rekognition!
Intro to Amazon Lightsail and Launching Your First Application on Amazon Ligh...Amazon Web Services
Amazon Lightsail is a simple cloud platform, designed to make running applications on AWS easy. Lightsail allows you to launch a virtual server with a few clicks and manage your infrastructure and applications from its intuitive interface. As your application grows, you can scale up easily with load balancers, attached block storage, and connections to other AWS services. With in-browser SSH and RDP access, easy server management, and in-console guidance, Lightsail provides all the tools needed for builders of all levels – no prior AWS experience required
This demo-focused session will walk you through the steps to launch an application on Lightsail and set up the framework used for the afternoon’s workshop. The session will also provide a brief overview of Amazon Rekogniton, AWS’s AI-driven image recognition service, which will help provide the ‘brains’ behind our workshop’s application.
Deep learning-based image recognition: Intro to Amazon Rekognition: Amazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos.
Deep Learning-based Image Recognition: Intro to Amazon RekognitionAmazon Web Services
by Mikhail Prudnikov, Solutions Architect, AWS
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos. Join this session and learn more about Amazon Rekognition!
MCL334_Find Missing Persons by Scanning Social Media with Amazon RekognitionAmazon Web Services
Every day hundreds of people disappear without a trace. In this workshop, you will work in teams to develop a solution that leverages Amazon Rekognition and other AWS services to analyze images from various sources (e.g., social media) and provide authorities with timely reports and alerts on new leads for missing individuals. The solution will entail a repeatable and automated process that follows best practices for architecting in the cloud, such as designing for high availability and scalability.
Build Computer Vision Applications with Amazon Rekognition: Machine Learning ...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Build Computer Vision Applications with Amazon Rekognition
Build Computer Vision Applications with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Level: 200-300
Speaker: Niranjan Hira - Solutions Architect, Amazon Lex
Announcing Amazon Rekognition - Deep Learning-Based Image Analysis - December...Amazon Web Services
Amazon Rekognition is a service that makes it easy to add image analysis to your applications. In this webinar you’ll learn how to detect objects, scenes, and faces in images. This webinar will also introduce Rekognition’s ability to search and compare faces. You’ll also be introduced to Rekognition’s API, which enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.
Learning Objectives:
• Learn about the capabilities and features of Amazon Rekognition
• Learn about the benefits of Amazon Rekognition
• Learn about the different use cases
• Learn how to get started using Amazon Rekognition
• Understand what is included in the AWS Free Tier and how to estimate usage costs
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
Level: 200-300
Speaker: Binoy Das - Partner Solutions Architect, AWS
Harnessing Artificial Intelligence in your Applications - Level 300Amazon Web Services
AWS offers a family of AI services that provide cloud-native Machine Learning and Deep Learning technologies allowing developers to build an entirely new generation of apps that can see, hear, speak, understand, and interact with the real world. In this session we take a look at Amazon Rekognition, Amazon Polly, and Amazon Lex.
Speakers:
Adam Larter, Developer Solutions Architect, Amazon Web Services
Alastair Cousins, Solutions Architect, Amazon Web Services
AWS re:Invent 2016: NEW LAUNCH! Introducing Amazon Rekognition (MAC203)Amazon Web Services
This session will introduce you to Amazon Rekognition, a new service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments.
To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos. Join this session and learn more about Amazon Rekognition!
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
Level: 200-300
Speaker: Liam Morrison - Principal Solutions Architect, AWS
Amazon recently published a new service, called Rekognition. We tried to use it on one project, and it's great. It enables you to add image analysis to your applications. With Rekognition, you can detect objects, scenes and faces in images. You can also search and compare faces. It is fast, reliable and integrates seamlessly with Amazon S3 and other AWS services. I will talk about the most interesting Rekognition's features and show you how we used it to improve our Face Matching algorithm in one app.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
2. Images – explosive growth trends
Source: InfoTrends Worldwide Consumer Photos Captured and Stored.
2013 -2017 prepared for Mylio.
3. Amazon Rekognition
Deep learning-based image recognition service
Search, verify, and organize millions of images
Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
6. Object and Scene Detection
Flower
ChairCoffee Table
Living Room
Indoors
7. Object and Scene Detection
Maple
Villa
Plant
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard
8. Using Rekognition Object and Scene Detection
Photo-sharing apps can power smart searches and
quickly find cherished memories, such as weddings,
hiking, or sunsets.
Vacation rental markets can automatically label host-
uploaded images with tags, such as fireplace, kitchen,
or swimming pool.
Travel sites and forums can classify user generated
images with labels such as beach, camping, or
mountains.
13. Using Rekognition Facial Analysis
Photo printing service can recommend the best
photos to their users
Online dating applications can improve their match
recommendations using face attributes
Retail businesses can understand the demographics
and sentiment of in-store customers
Ad-tech services can display dynamic and
personalized content to customers
14. Facial Analysis - Use Case
(Retail – In-store and Online)
Demographic and Sentiment Analysis
Female
Happy
Smiling
Male
No Facial Hair
Happy
Female
Sad
No Eyeglasses
15. Look Your Best All Day
Time for A New Look?
Facial Analysis - Use Case
(Targeted Marketing)
Demographic and Sentiment Analysis
PersonAPersonB
Sees
Sees
19. IoT and camera manufacturers can integrate face-
based verification directly into their products
Application of face comparison in locating person of
interest for Public Safety
Hotels & hospitality businesses can provide seamless
access for guests and VIPs
Online exams or polls can verify presence of registered
person by comparing against image captured by
webcam.
Using Rekognition Face Comparison
20. Amazon Rekognition API
Facial Recognition
Index and Search faces in a collection
Index
Search
Collection
IndexFaces
SearchFacesByImage
24. Using Facial Recognition
Family photo sharing apps can use face recognition to
group all faces of the same person in a family
Entertainment and news organizations can index decades
of archived images to find celebrities
Secure campuses / workplaces can use face search to
ensure all personnel in their facilities are authorized to be
there
Public safety teams can leverage face collections to
automate tracking of persons of interest
27. Traditional Search Engine Basics
• Vector space model
• Cosine Similarity
q
documents and queries are represented as vectors
src: https://en.wikipedia.org/wiki/Vector_space_model
28. Document Representation
• Convert a document into a list of words or terms.
• But Images don’t have terms!
• Rekognition gives us labels with confidence
• This can be used as a proxy for terms and term
frequency
quick oak is called
Freq 4 2 1 1
The Oak is called the king
of trees.
The Oak quivers in the
breeze,
{
"Confidence":
94.62968444824219,
"Name": "adventure"
},
{
"Confidence":
94.62968444824219,
"Name": "boat"
},
{
"Confidence":
94.62968444824219,
"Name": "rafting"
},
. . .
32. Amazon Rekognition – Availability and Pricing
1. Released General Availability
2. Available in 3 regions,
1. US East (N. Virginia)
2. US West (Oregon)
3. EU (Ireland)
3. Pricing
• Pay as you go
• Free Tier – 5000 images per month for first 12 months
• Tiered Pricing designed
33. Amazon Rekognition – Pricing Details
*Images processed: For APIs with image as input, it’s the number of images analyzed. For APIs with no image input 1 API call = 1 image processed.
Image Analysis Tiers
Price per 1000
images processed
First 1 million images processed* per month $1.00
Next 9 million images processed* per month $0.80
Next 90 million images processed* per month $0.60
Over 100 million images processed* per month $0.40
*Images processed: For APIs with image as input, it’s the number of images analyzed. For APIs with no image input 1 API call = 1 image processed.
34. Summary
1. Fully managed and easy-to-use image recognition service
2. Four primary capabilities
1. Object and Scene Detection
2. Facial Analysis
3. Face Comparison
4. Facial Recognition
3. Integrated with AWS and AI Services
• Amazon S3
• Lex and Polly
4. Scalable and low cost
In the last decade we have witnessed an explosion in the amount of images and video that are digitally available.
“Smart Cities Will Use 1.6 Billion Connected Things in 2016”
“In 2016, commercial security cameras, webcams, and indoor LEDs will drive total growth, representing 24 percent of the IoT market for smart cities.”
- Gartner*, December 7, 2015
Rob van der Meulen & Viveca Woods
Introducing Amazon Rekognition - a fully managed deep learning based image recognition service. Rekognition was designed from the get-go to run at scale. It comprehends scenes, objects, concepts and faces. Given an image, it will return a list of labels. Given an image with one or more faces,it will return bounding boxes for each face, along with face attributes. Given two images with faces, it will compare the largest face from the source image and find similarity with faces found in the tagret image. Rekognition provides quality face recognition at scale, and supports creation of collection of millions of faces and search of similar faces in the collection.
Now lets dive into each of these features and look at the API that support these features.
11/22- Removed “thousands of “
Objects and Scene Detection - This features allows you to detect thousands of objects, scenes and concepts in your images.
The API is DetectLabels.
In its simplest form – DetectLabels takes an image as input and returns a set of labels with confidence score.
Request
{ "Image": { "Bytes": blob, "S3Object": { "Bucket": "string", "Name": "string", "Version": "string" } }, "MaxLabels": number, "MinConfidence": number }
Response
{ "Labels": [ { "Confidence": number, "Name": "string" } ], "OrientationCorrection": "string" }
In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.
Lets looks at a couple of example of images
11/22 Removed image. Add image that we have the rights to. (removed image because it didn’t add value)
You can see how easily Rekognition’s object and scene detection can be use for different segments. Consumer, Ad tech, Public Safety, and media are just a few examples.
Original content:
Consumer applications can use labels generated from images to power smart search interfaces for users
Advertising companies can use metadata from images to improve their targeting algorithms
Public safety teams can identify and label objects in security images to rapidly filter and search
News organizations can to quickly search their archives to find photos to match breaking news
Using Amazon DetectFaces API, you can detect faces in an image and key facial characteristics.
DetectFaces taken an image with one or more faces and returns bounding box of the faces and some key landmarks and attributes for each face detected.
Input
{ "Attributes": [ "string" ], "Image": { "Bytes": blob, "S3Object": { "Bucket": "string", "Name": "string", "Version": "string" } } }
Talk about the attributes.
At a high level face attributes provides you with demographic data, sentiment of the face, face quality and landmarks.
Facial Analysis is seeing utility in Real Estate, Social, Consumer, retail and Ad-tech
Original content:
Online dating applications can improve their algorithms and member’s match success rates by tracking sentiment and poses
Consumer photo sites can identify and recommend the best photos to their users
Understand the demographics and sentiment of in-store customers to better align products and services
Display personalized content to customers in near real time
automatically analyze the facial pose and sentiment in each photo of a member’s profile and track to see what aspects correspond with the most successful swipe metrics
Application: The application uses in-store cameras to capture live images of shoppers in a retail store
Rekognition: DetectFaces analyzes the image and returns facial attributes detected, which include emotion and demographic detail
Redshift: The data is stored in Redshift to make it easy to analyze over time
QuickSight: run periodic analysis and reporting to identify trends in demographic activity and in-store sentiment over time
Expand to include AB analysis and testing to determine the most effective content to show to each demographic group
Application: The application’s image indexing function reads image files in S3 and sends them to Rekognition
Rekognition: IndexFaces extracts the facial feature vectors, stores them into a face collection, and returns a unique faceID for each discovered face
Rekognition: Face collections are owned and managed by the user
Application: the application records the faceID along with known attributes of the person in an application-managed database table
Democratizing Image Analysis (key factors: affordable, accessible, scalable) that helps you get started in minutes
Democratizing Image Analysis (key factors: affordable, accessible, scalable) that helps you get started in minutes
Democratizing Image Analysis (key factors: affordable, accessible, scalable) that helps you get started in minutes