Detect domain-specific content
In addition to tagging and high-level categorization, Computer Vision also supports further domain-specific analysis using models that have been trained on specialized data.
There are two ways to use the domain-specific models: by themselves (scoped analysis) or as an enhancement to the categorization feature.
Scoped analysis
You can analyze an image using only the chosen domain-specific model by calling the Models/<model>/Analyze API.
The following is a sample JSON response returned by the models/celebrities/analyze API for the given image:
{
"result": {
"celebrities": [{
"faceRectangle": {
"top": 391,
"left": 318,
"width": 184,
"height": 184
},
"name": "Satya Nadella",
"confidence": 0.99999856948852539
}]
},
"requestId": "8217262a-1a90-4498-a242-68376a4b956b",
"metadata": {
"width": 800,
"height": 1200,
"format": "Jpeg"
}
}
Enhanced categorization analysis
You can also use domain-specific models to supplement general image analysis. You do this as part of high-level categorization by specifying domain-specific models in the details parameter of the Analyze API call.
In this case, the 86-category taxonomy classifier is called first. If any of the detected categories have a matching domain-specific model, the image is passed through that model as well and the results are added.
The following JSON response shows how domain-specific analysis can be included as the detail
node in a broader categorization analysis.
"categories":[
{
"name":"abstract_",
"score":0.00390625
},
{
"name":"people_",
"score":0.83984375,
"detail":{
"celebrities":[
{
"name":"Satya Nadella",
"faceRectangle":{
"left":597,
"top":162,
"width":248,
"height":248
},
"confidence":0.999028444
}
],
"landmarks":[
{
"name":"Forbidden City",
"confidence":0.9978346
}
]
}
}
]
List the domain-specific models
Currently, Computer Vision supports the following domain-specific models:
Name | Description |
---|---|
celebrities | Celebrity recognition, supported for images classified in the people_ category |
landmarks | Landmark recognition, supported for images classified in the outdoor_ or building_ categories |
Calling the Models API will return this information along with the categories to which each model can apply:
{
"models":[
{
"name":"celebrities",
"categories":[
"people_",
"人_",
"pessoas_",
"gente_"
]
},
{
"name":"landmarks",
"categories":[
"outdoor_",
"户外_",
"屋外_",
"aoarlivre_",
"alairelibre_",
"building_",
"建筑_",
"建物_",
"edifício_"
]
}
]
}
Next steps
Learn concepts about categorizing images.
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