improve image ret concept

Author: PatrickFarley

articles/cognitive-services/Computer-vision/concept-image-retrieval.md

@@ -43,9 +43,11 @@ Vector embeddings are a way of representing content—text or images—a
 
 ## How does it work? 
 
+:::image type="content" source="media/image-retrieval.png" alt-text="Diagram of image retrieval process.":::
+
 1. Vectorize Images and Text: the Image Retrieval APIs, **VectorizeImage** and **VectorizeText**, can be used to extract feature vectors out of an image or text respectively. The APIs return a single feature vector representing the entire input.
-- Measure similarity: Vector search systems typically use distance metrics, such as cosine distance or Euclidean distance, to compare vectors and rank them by similarity. The [Vision studio](https://portal.vision.cognitive.azure.com/) demo uses [cosine distance](./how-to/image-retrieval.md#calculate-vector-similarity) to measure similarity.  
-- Retrieve Images: Use the top _N_ vectors similar to the search query and retrieve images corresponding to those vectors from your photo library to  provide as the final result.
+1. Measure similarity: Vector search systems typically use distance metrics, such as cosine distance or Euclidean distance, to compare vectors and rank them by similarity. The [Vision studio](https://portal.vision.cognitive.azure.com/) demo uses [cosine distance](./how-to/image-retrieval.md#calculate-vector-similarity) to measure similarity.  
+1. Retrieve Images: Use the top _N_ vectors similar to the search query and retrieve images corresponding to those vectors from your photo library to  provide as the final result.
 
 ## Next steps