Get labels identification insights

Article
10/09/2024

Labels identification

Labels identification is an Azure AI Video Indexer AI feature that identifies visual objects like sunglasses or actions like swimming, appearing in the video footage of a media file. There are many labels identification categories and once extracted, labels identification instances are displayed in the Insights tab and can be translated into over 50 languages. Clicking a Label opens the instance in the media file, select Play Previous or Play Next to see more instances.

Labels identification use cases

Extracting labels from frames for contextual advertising or branding. For example, placing an ad for beer following footage on a beach.
Creating a verbal description of footage to enhance accessibility for the visually impaired, for example a background storyteller in movies.
Deep searching media archives for insights on specific objects to create feature stories for the news.
Using relevant labels to create content for trailers, highlights reels, social media, or new clips.

View the insight JSON with the web portal

Once you have uploaded and indexed a video, insights are available in JSON format for download using the web portal.

Select the Library tab.
Select media you want to work with.
Select Download and the Insights (JSON). The JSON file opens in a new browser tab.
Look for the key pair described in the example response.

Use the API

Use the Get Video Index request. We recommend passing &includeSummarizedInsights=false.
Look for the key pairs described in the example response.

Example response

    "labels": [
        {
        "id": 1,
        "name": "human face",
        "language": "en-US",
        "instances": [
            {
            "confidence": 0.9987,
            "adjustedStart": "0:00:00",
            "adjustedEnd": "0:00:25.6",
            "start": "0:00:00",
            "end": "0:00:25.6"
            },
            {
            "confidence": 0.9989,
            "adjustedStart": "0:01:21.067",
            "adjustedEnd": "0:01:41.334",
            "start": "0:01:21.067",
            "end": "0:01:41.334"
            }
        ]
        },
        {
        "id": 2,
        "name": "person",
        "referenceId": "person",
        "language": "en-US",
        "instances": [
            {
            "confidence": 0.9959,
            "adjustedStart": "0:00:00",
            "adjustedEnd": "0:00:26.667",
            "start": "0:00:00",
            "end": "0:00:26.667"
            },
            {
            "confidence": 0.9974,
            "adjustedStart": "0:01:21.067",
            "adjustedEnd": "0:01:41.334",
            "start": "0:01:21.067",
            "end": "0:01:41.334"
            }
        ]
        },

Important

It is important to read the transparency note overview for all VI features. Each insight also has transparency notes of its own:

Labels identification notes

Carefully consider the accuracy of the results, to promote more accurate detections, check the quality of the video, low quality video might affect the detected insights.
Carefully consider when using for law enforcement that Labels potentially can't detect parts of the video. To ensure fair and high-quality decisions, combine Labels with human oversight.
Don't use labels identification for decisions that might have serious adverse impacts. Machine learning models can result in undetected or incorrect classification output. Decisions based on incorrect output could have serious adverse impacts. Additionally, it's advisable to include human review of decisions that have the potential for serious impacts on individuals.

Labels identification components

During the Labels procedure, objects in a media file are processed, as follows:

Component	Definition
Source	The user uploads the source file for indexing.
Tagging	Images are tagged and labeled. For example, door, chair, woman, headphones, jeans.
Filtering and aggregation	Tags are filtered according to their confidence level and aggregated according to their category.
Confidence level	The estimated confidence level of each label is calculated as a range of 0 to 1. The confidence score represents the certainty in the accuracy of the result. For example, an 82% certainty is represented as an 0.82 score.

Sample code

See all samples for VI

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