When working with Azure Computer Vision services, including image analysis features in your API requests allows you to specify exactly what type of visual information you want to extract from images. This targeted approach optimizes both performance and cost efficiency.
Azure's Analyze Image API a…When working with Azure Computer Vision services, including image analysis features in your API requests allows you to specify exactly what type of visual information you want to extract from images. This targeted approach optimizes both performance and cost efficiency.
Azure's Analyze Image API accepts a 'features' parameter that determines which visual aspects the service will examine. The available features include:
**Categories**: Classifies images into a taxonomy of categories like buildings, people, or outdoor scenes.
**Tags**: Provides content tags that describe objects, living beings, scenery, and actions detected in the image.
**Description**: Generates human-readable sentences describing the image content with confidence scores.
**Faces**: Detects human faces and returns coordinates, gender, and estimated age.
**Objects**: Identifies objects within the image and provides bounding box coordinates for each detected item.
**Brands**: Recognizes commercial logos and brands present in images.
**Adult**: Evaluates whether content contains adult, racy, or gory material.
**Color**: Analyzes dominant colors, accent colors, and determines if an image is black and white.
**ImageType**: Identifies whether an image is clip art or a line drawing.
**Read**: Extracts printed and handwritten text using OCR capabilities.
To include these features in your request, you append them as query parameters or include them in the request body. For example, a REST API call might look like: `https://[endpoint]/vision/v3.2/analyze?visualFeatures=Tags,Description,Objects`
When using SDKs like Python or C#, you pass a list of feature enums to the analyze method. Best practice involves requesting only the features you need, as each additional feature increases processing time and may affect billing. You can combine multiple features in a single request for comprehensive analysis while maintaining efficient resource utilization.
Including Image Analysis Features in Requests - Complete Guide for AI-102
Why Is This Important?
Understanding how to include image analysis features in requests is fundamental for the AI-102 exam because Azure AI Vision services require you to explicitly specify which visual features you want to analyze. This knowledge directly impacts how you build efficient, cost-effective computer vision solutions. Each feature you request affects both the response content and the API call cost.
What Are Image Analysis Features?
Image analysis features are specific visual attributes that the Azure AI Vision API can detect and return in its response. These features include:
• Tags - Content tags for thousands of recognizable objects, living beings, scenery, and actions • Objects - Detects objects within an image with bounding box coordinates • Caption - Generates a human-readable sentence describing the image content • Dense Captions - Provides detailed descriptions for multiple regions in the image • Read - Extracts printed and handwritten text (OCR) • Smart Crops - Suggests crop regions based on the area of interest • People - Detects people in images with bounding boxes • Brands - Identifies commercial brand logos • Categories - Categorizes image content using a taxonomy • Color - Determines the accent color, dominant colors, and whether an image is black and white • Image Type - Detects if the image is clip art or a line drawing • Adult - Detects adult, racy, or gory content • Faces - Basic face detection with age and gender estimation
How It Works
When making a request to the Azure AI Vision API, you specify the desired features using the visualFeatures parameter. Here is an example REST API call:
POST https://{endpoint}/computervision/imageanalysis:analyze?api-version=2023-10-01&features=tags,caption,objects
For the Image Analysis 4.0 API, you use a comma-separated list of features in the query string. The SDK equivalent in C# would be:
ImageAnalysisResult result = client.Analyze(imageUrl, VisualFeatures.Caption | VisualFeatures.Tags | VisualFeatures.Objects);
The response contains only the data for the features you requested, making it essential to request all needed features in a single call for efficiency.
Key Implementation Considerations
• Combining Features - You can request multiple features in a single API call to reduce latency and costs • Model Version - Use the model-version parameter to specify which model version to use • Language Support - Use the language parameter to get results in supported languages • Gender-Neutral Captions - The gender-neutral-caption parameter enables inclusive descriptions
Exam Tips: Answering Questions on Including Image Analysis Features
1. Know the exact parameter names - The exam may test whether you know that visualFeatures is the correct parameter name for specifying features
2. Understand feature capabilities - Be clear on what each feature returns. For example, Objects returns bounding boxes while Tags returns confidence scores
3. API version awareness - Know the differences between Image Analysis 3.2 and 4.0 APIs. The 4.0 version uses a different endpoint structure and feature set
4. SDK vs REST - Questions may present code snippets. Recognize both REST query parameters and SDK method patterns
5. Efficiency scenarios - When asked about optimizing API calls, remember that combining multiple features in one request is more efficient than separate calls
6. Feature limitations - Some features work only with certain image formats or sizes. Know that images must be at least 50x50 pixels
7. Cost implications - Understand that requesting more features increases processing but is still cheaper than multiple separate calls
8. Read carefully - If a question asks about text extraction, the correct feature is Read, not Tags or Caption
9. Confidence scores - Many features return confidence values between 0 and 1. Know how to interpret and filter results based on thresholds