comvis freshness

Author: PatrickFarley

articles/ai-services/computer-vision/concept-brand-detection.md

@@ -8,17 +8,19 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 01/22/2025
 ms.author: pafarley
 ---
 
 # Brand detection
 
-Brand detection is a specialized mode of [object detection](concept-object-detection.md) that uses a database of thousands of global logos to identify commercial brands in images or video. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement.
+Brand detection is a specialized mode of [object detection](concept-object-detection.md) that uses a database of thousands of global corporate logos to identify commercial brands in images or video. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement.
+
+## How it works
 
 The Azure AI Vision service detects whether there are brand logos in a given image. If a brand logo is detected, the service returns the brand name, a confidence score, and the coordinates of a bounding box around the logo.
 
-The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the Vision service doesn't detect the brand you're looking for, you could also try creating and training your own logo detector using the [Custom Vision](../custom-vision-service/index.yml) service.
+The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the Vision service doesn't detect the brand you're looking for, you can also try creating and training your own logo detector using the [Custom Vision](../custom-vision-service/index.yml) service.
 
 ## Brand detection example
 
@@ -71,4 +73,7 @@ In some cases, the brand detector picks up both the logo image and the stylized
 
 The brand detection feature is part of the [Analyze Image](/rest/api/computervision/analyze-image) API. You can call this API by using a native SDK or through REST calls. Include `Brands` in the `visualFeatures` query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"brands"` section.
 
-* [Quickstart: Image Analysis](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
+## Next step
+
+> [!div class="nextstepaction"]
+> [Quickstart: Image Analysis](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-detecting-adult-content.md

@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 01/22/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -37,4 +37,7 @@ The "adult" classification contains several different categories:
 
 You can detect adult content with the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API. When you add the value of `Adult` to the **visualFeatures** query parameter, the API returns three boolean properties—`isAdultContent`, `isRacyContent`, and `isGoryContent`—in its JSON response. The method also returns corresponding properties—`adultScore`, `racyScore`, and `goreScore`—which represent confidence scores between zero and one for each respective category.
 
-- [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
+## Next step
+
+> [!div class="nextstepaction"]
+> [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-detecting-color-schemes.md

@@ -80,4 +80,7 @@ The following table shows Azure AI Vision's black and white evaluation in the sa
 
 The color scheme detection feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API. You can call this API through a native SDK or through REST calls. Include `Color` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"color"` section.
 
-* [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
+## Next step
+
+> [!div class="nextstepaction"]
+> [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-detecting-faces.md

@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 01/22/2025
 ms.author: pafarley
 ---
 
@@ -112,4 +112,7 @@ The next example demonstrates the JSON response returned for an image containing
 
 The face detection feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP) API. You can call this API through a native SDK or through REST calls. Include `Faces` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"faces"` section.
 
-* [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
+## Next step
+
+> [!div class="nextstepaction"]
+> [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-people-detection.md

@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 01/22/2025
 ms.author: pafarley
 ---
 
@@ -79,6 +79,7 @@ The following JSON response illustrates what the Analysis 4.0 API returns when d
 
 The people detection feature is part of the [Analyze Image 4.0 API](https://aka.ms/vision-4-0-ref). Include `People` in the **features** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"people"` section.
 
-## Next steps
+## Next step
 
-* [Call the Analyze Image API](./how-to/call-analyze-image-40.md)
+> [!div class="nextstepaction"]
+> [Call the Analyze Image API](./how-to/call-analyze-image-40.md)

articles/ai-services/computer-vision/how-to/video-retrieval.md

@@ -1,45 +1,48 @@
 ---
 title: Do video retrieval using vectorization - Image Analysis 4.0
 titleSuffix: Azure AI services
-description: Learn how to call the Video Retrieval APIs to vectorize video frames and search terms.
+description: Learn how to use the Video Retrieval APIs to vectorize video frames and search terms, and perform searches using metadata and features.
 #services: cognitive-services
+#customer intent: As a developer, I want to use Video Retrieval APIs to enhance video search capabilities with metadata and features.
 author: PatrickFarley
 manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 10/16/2023
+ms.date: 01/22/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
 
 # Do video retrieval using vectorization (version 4.0 preview)
 
-Azure AI Video Retrieval APIs are part of Azure AI Vision and enable developers to create an index, add documents (videos and images) to it, and search with natural language. Developers can define metadata schemas for each index and ingest metadata to the service to help with retrieval. Developers can also specify what features to extract from the index (vision, speech) and filter their search based on features.
+Azure AI Video Retrieval APIs are part of Azure AI Vision and enable developers to create a media index, add documents (videos and images) to it, and search it with natural language. Developers can define metadata schemas for each index and ingest metadata to the service to help with retrieval. Developers can also specify which features to extract from the index (vision, speech) and filter their search based on features.
 
 ## Prerequisites
 
 - Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services).
 - Once you have your Azure subscription, [create a Vision resource using the portal](/azure/cognitive-services/cognitive-services-apis-create-account). For this preview, you must create your resource in the one of the following regions - Australia East, Switzerland North, Sweden Central, or East US.
 - An Azure Storage resource - [Create one](/azure/storage/common/storage-account-create?tabs=azure-portal)
 
-## Input requirements
+## Select a video
+
+Refer to the video input requirements:
 
 [!INCLUDE [video-retrieval-input](../includes/video-retrieval-input.md)]
 
 ## Call the Video Retrieval APIs
 
-To use the Video Retrieval APIs in a typical pattern, you would do the following steps:
+To use the Video Retrieval APIs in a typical pattern, you'll follow these steps:
 
 1. Create an index using **PUT - Create an index**.
 2. Add video documents to the index using **PUT - CreateIngestion**.
 3. Wait for the ingestion to complete, checking with **GET - ListIngestions**.
 4. Search for a keyword or phrase using **POST - SearchByText**.
 
 
-### Use Video Retrieval APIs for metadata-based search
+### Example: Use Video Retrieval APIs for metadata-based search
 
-The Video Retrieval APIs allows a user to add metadata to video files. Metadata is additional information associated with video files such as "Camera ID," "Timestamp," or "Location" that can be used to organize, filter, and search for specific videos. This example demonstrates how to create an index, add video files with associated metadata, and perform searches using different features.
+The Video Retrieval APIs allow a user to add metadata to video files. Metadata is additional information associated with video files such as "Camera ID," "Timestamp," or "Location" that can be used to organize, filter, and search for specific videos. This example demonstrates how to create an index, add video files with associated metadata, and perform searches using different features.
 
 ### Step 1: Create an Index
 
@@ -77,7 +80,7 @@ curl.exe -v -X PUT "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexes
 ```
 
 **Response:**
-```
+```console
 HTTP/1.1 201 Created
 Content-Length: 530
 Content-Type: application/json; charset=utf-8
@@ -156,7 +159,7 @@ curl.exe -v -X PUT "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexes
 ```
 
 **Response:**
-```
+```console
 HTTP/1.1 202 Accepted
 Content-Length: 152
 Content-Type: application/json; charset=utf-8
@@ -184,7 +187,7 @@ curl.exe -v -X GET "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexes
 ```
 
 **Response:**
-```
+```console
 HTTP/1.1 200 OK
 Content-Length: 164
 Content-Type: application/json; charset=utf-8
@@ -233,7 +236,7 @@ curl.exe -v -X POST "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexe
 ```
 
 **Response:**
-```
+```console
 HTTP/1.1 200 OK
 Content-Length: 3289
 Content-Type: application/json; charset=utf-8
@@ -297,7 +300,7 @@ curl.exe -v -X POST "https://<YOUR_ENDPOINT_URL>com/computervision/retrieval/ind
 ```
 
 **Response:**
-```
+```console
 HTTP/1.1 200 OK
 Content-Length: 49001
 Content-Type: application/json; charset=utf-8
@@ -337,6 +340,7 @@ Connection: close
 }
 ```
 
-## Next steps
+## Next step
 
-[Multimodal embeddings concepts](../concept-image-retrieval.md)
+> [!div class="nextstepaction"]
+> [Multimodal embeddings concepts](../concept-image-retrieval.md)

articles/ai-services/computer-vision/use-case-identity-verification.md

@@ -10,13 +10,13 @@ ms.service: azure-ai-vision
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 01/22/2025
 ms.author: pafarley
 ---
 
 # Overview: Verification with Face 
 
-Provide the best-in-class face verification experience in your business solution using the Azure AI Face service. You can verify a user's face against a government-issued ID card like a passport or driver's license. Use this verification to grant access to digital or physical services or recover an account. Specific access scenarios include opening a new account, verifying a user, or proctoring an online assessment. Verification can be done when a person is onboarded to your service, and repeated when they access a digital or physical service.
+Azure AI Face service lets you provide the best-in-class face verification experience in your business solution. Use verification to grant access to digital or physical services or recover an account. For example, you can verify a user's face against a government-issued ID card like a passport or driver's license. Specific access scenarios include opening a new account, verifying a user, or proctoring an online assessment. Verification can be done when a person is onboarded to your service and repeated when they access a digital or physical service.
 
 :::image type="content" source="media/use-cases/face-recognition.png" alt-text="Photo of a person holding a phone up to his face to take a picture":::
 
@@ -36,7 +36,7 @@ Face service can power an end-to-end, low-friction, high-accuracy, verification
 * Face Verification ("Verification" / "Verify") builds on Detect and addresses the question, "Are these two images of the same person?" Verification is also called "one-to-one" matching because the probe image is compared to only one enrolled template. Verification can be used in access control scenarios to verify that a picture matches a previously captured image (such as from a photo from a government-issued ID card).
 * Face Group ("Group") also builds on Detect and creates smaller groups of faces that look similar to each other from all enrollment templates.  
 
-## Next steps
+## Next step
 
 Follow a quickstart to do verification with Face.