Amazon Rekognition | Object and Scene Detection Flashcards
In which AWS regions is Amazon Rekognition available?
Object and Scene Detection
Amazon Rekognition | Machine Learning
Amazon Rekognition Image is currently available in the US East (Northern Virginia), US West (Oregon), US East (Ohio) , EU (Ireland), Asia Pacific (Tokyo), Asia Pacific (Sydney) and the AWS GovCloud (US) regions. Amazon Rekognition Video is available in US East (Northern Virginia), US West (Oregon), US East (Ohio) , EU (Ireland), Asia Pacific (Tokyo) and Asia Pacific (Sydney) regions. Amazon Rekognition Video real-time streaming is only available in US East (Northern Virginia), US West (Oregon), EU (Ireland) and Asia Pacific (Tokyo) regions.
What is a label?
Object and Scene Detection
Amazon Rekognition | Machine Learning
A label is an object, scene, or concept found in an image based on its contents. For example, a photo of people on a tropical beach may contain labels such as ‘Person’, ‘Water’, ‘Sand’, ‘Palm Tree’, and ‘Swimwear’ (objects), ‘Beach’ (scene), and ‘Outdoors’ (concept).
What is a confidence score and how do I use it?
Object and Scene Detection
Amazon Rekognition | Machine Learning
A confidence score is a number between 0 and 100 that indicates the probability that a given prediction is correct. In the tropical beach example, if the object and scene detection process returns a confidence score of 99 for the label ‘Water’ and 35 for the label ‘Palm Tree’, then it is more likely that the image contains water but not a palm tree.
Applications that are very sensitive to detection errors (false positives) should discard results associated with confidence scores below a certain threshold. The optimum threshold depends on the application. In many cases, you will get the best user experience by setting minimum confidence values higher than the default value.
What is Object and Scene Detection?
Object and Scene Detection
Amazon Rekognition | Machine Learning
Object and Scene Detection refers to the process of analyzing an image or video to assign labels based on its visual content. Amazon Rekognition Image does this through the DetectLabels API. This API lets you automatically identify thousands of objects, scenes, and concepts and returns a confidence score for each label. DetectLabels uses a default confidence threshold of 50. Object and Scene detection is ideal for customers who want to search and organize large image libraries, including consumer and lifestyle applications that depend on user-generated content and ad tech companies looking to improve their targeting algorithms.
What types of labels does Amazon Rekognition support?
Object and Scene Detection
Amazon Rekognition | Machine Learning
Rekognition supports thousands of labels belonging to common categories including, but not limited to:
People and Events: ‘Wedding’, ‘Bride’, ‘Baby’, ‘Birthday Cake’, ‘Guitarist’, etc.
Food and Drink: ‘Apple’, ‘Sandwich’, ‘Wine’, ‘Cake’, ‘Pizza’, etc.
Nature and Outdoors: ‘Beach’, ‘Mountains’, ‘Lake’, ‘Sunset’, ‘Rainbow’, etc.
Animals and Pets: ‘Dog’, ‘Cat’, ‘Horse’, ‘Tiger’, ‘Turtle’, etc.
Home and Garden: ‘Bed’, ‘Table’, ‘Backyard’, ‘Chandelier’, ‘Bedroom’, etc.
Sports and Leisure: ‘Golf’, ‘Basketball’, ‘Hockey’, ‘Tennis’, ‘Hiking’, etc.
Plants and Flowers: ‘Rose’, ‘Tulip’, ‘Palm Tree’, ‘Forest’, ‘Bamboo’, etc.
Art and Entertainment: ‘Sculpture’, ‘Painting’, ‘Guitar’, ‘Ballet’, ‘Mosaic’, etc.
Transportation and Vehicles: ‘Airplane’, ‘Car’, ‘Bicycle’, ‘Motorcycle’, ‘Truck’, etc.
Electronics: ‘Computer’, ‘Mobile Phone’, ‘Video Camera’, ‘TV’, ‘Headphones’, etc.
How is Object and Scene Detection different for video analysis?
Object and Scene Detection
Amazon Rekognition | Machine Learning
Rekognition Video enables you to automatically identify thousands of objects - such as vehicles or pets - and activities - such as celebrating or dancing - and provides you with timestamps and a confidence score for each label. It also relies on motion and time context in the video to accurately identify complex activities, such as “blowing a candle” or “extinguishing fire”.
