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Capture bias

As explained in Delivering Machine Learning Projects: From design to deployment

Capture bias is systematic distortion caused by the image positions, viewpoints, or presentation conditions represented in visual data.

Capture bias is systematic distortion caused by where labeled objects appear, how they are viewed, and which presentation conditions dominate an image dataset. It can make a model learn camera or scene patterns instead of the intended object. section 6.1.4

Inspect capture bias during exploratory data analysis by checking:

  • Viewpoint and orientation, such as side-on or showroom presentations and consistently right-facing mug handles. section 6.1.4
  • Image positioning, including which regions of the frame contain labeled objects. section 6.1.4
  • Repeated environments or backgrounds associated with particular labels. section 6.1.4

A misleading environmental cue

A purported tank classifier may learn snow as a tank signal and sunshine as a tractor signal when those environments dominate their respective training images. section 6.1.4

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Defines capture bias and gives inspection checks, but stops short of prescribing corrective sampling or measurement procedures.
Examples
Uses right-facing coffee mugs, showroom views, and snow-associated tanks versus sunshine-associated tractors.
What it assumes
Assumes familiarity with labeled image datasets and teaches visual inspection of their systematic presentation patterns.
What you can do after
Evaluate image datasets for viewpoint, positioning, and background patterns that may create misleading predictive signals.
How it connects
Connects capture bias with orientation, image positioning, environment, class balance, labels, coverage, and exploratory data analysis.
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