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Copy file name to clipboardExpand all lines: articles/ai-services/computer-vision/how-to/blob-storage-search.md
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title: Configure your blob storage container for image retrieval and video search
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title: Configure your blob storage container for image retrieval
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titleSuffix: Azure AI services
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description: Configure your Azure storage account to get started with the **Search photos image retrieval** experience in Vision Studio.
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#services: cognitive-services
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# Configure your blob storage for image retrieval and video search in Vision Studio
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# Configure your blob storage for image retrieval in Vision Studio
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To get started with the **Search photos image retrieval** scenario in Vision Studio, you need to select or create a new Azure storage account. Your storage account can be in any region, but creating it in the same region as your Vision resource is more efficient and reduces cost.
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To get started with the **Search photos with image retrieval** scenario in Vision Studio, you need to select or create a new Azure storage account. Your storage account can be in any region, but creating it in the same region as your Vision resource is more efficient and reduces cost.
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> [!IMPORTANT]
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> You need to create your storage account on the same Azure subscription as the Vision resource you're using in the **Search photos image retrieval** scenario as shown below.
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:::image type="content" source="../media/storage-instructions/subscription.png" alt-text="Screenshot of resource selection.":::
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> You need to create your storage account on the same Azure subscription as the Vision resource you're using in the **Search photos with image retrieval** scenario.
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>
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> :::image type="content" source="../media/storage-instructions/subscription.png" alt-text="Screenshot of resource selection.":::
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## Create a new storage account
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:::image type="content" source="../media/storage-instructions/cors-rule.png" alt-text="Screenshot of completed CORS screen.":::
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This allows Vision Studio to access images and videos in your blob storage container to extract insights on your data.
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## Upload images and videos in Vision Studio
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In the **Try with your own video** or **Try with your own image** section in Vision Studio, select the storage account that you configured with the CORS rule. Select the container in which your images or videos are stored. If you don't have a container, you can create one and upload the images or videos from your local device. If you have updated the CORS rules on the storage account, refresh the Blob container or Video files on container sections.
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This allows Vision Studio to access images in your blob storage container to extract insights on your data.
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:::image type="content" source="../media/storage-instructions/video-selection.png" alt-text="Screenshot of image upload in Vision Studio.":::
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## Upload images in Vision Studio
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In the **Search photos with image retrieval** section in Vision Studio, select the storage account that you configured with the CORS rule. Select the container in which your images are stored. If you don't have a container, you can create one and upload the images from your local device. If you have updated the CORS rules on the storage account, refresh the Blob container or Video files on container sections.
Copy file name to clipboardExpand all lines: articles/ai-services/computer-vision/how-to/image-retrieval.md
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ms.service: azure-ai-vision
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ms.topic: how-to
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ms.date: 01/19/2024
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ms.date: 01/30/2024
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ms.author: pafarley
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ms.custom: references_regions
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Cosine similarity is a method for measuring the similarity of two vectors. In an image retrieval scenario, you'll compare the search query vector with each image's vector. Images that are above a certain threshold of similarity can then be returned as search results.
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The following example C# code calculates the cosine similarity between two vectors. It's up to you to decide what similarity threshold to use for returning images as search results.
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The following example code calculates the cosine similarity between two vectors. It's up to you to decide what similarity threshold to use for returning images as search results.
Copy file name to clipboardExpand all lines: articles/ai-services/computer-vision/includes/identity-curl-quickstart.md
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Save the IDs of each **Person** created; it's important to keep track of which person name has which ID.
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1. Next you'll need to detect new faces and associate them with the **Person** objects that exist. The following command detects a face from the image *Family1-Dad.jpg* and adds it to the corresponding person. You need to specify the `personId` as the ID that was returned when you created the "Family1-Dad" **Person** object. The image name corresponds to the name of the created **Person**. Also enter the **LargePersonGroup** ID and your key in the appropriate fields.
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1. Next you'll need to detect new faces and associate them with the **Person** objects that exist. The following command detects a face from the image *Family1-Dad1.jpg* and adds it to the corresponding person. You need to specify the `personId` as the ID that was returned when you created the "Family1-Dad" **Person** object. The image name corresponds to the name of the created **Person**. Also enter the **LargePersonGroup** ID and your key in the appropriate fields.
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