  In many applications, machine learned (ML) models are required to hold some
invariance qualities, such as rotation, size, intensity, and background
invariance. Unlike many types of variance, the variants of background scenes
cannot be ordered easily, which makes it difficult to analyze the robustness
and biases of the models concerned. In this work, we present a technical
solution for ordering background scenes according to their semantic proximity
to a target image that contains a foreground object being tested. We make use
of the results of object recognition as the semantic description of each image,
and construct an ontology for storing knowledge about relationships among
different objects using association analysis. This ontology enables (i)
efficient and meaningful search for background scenes of different semantic
distances to a target image, (ii) quantitative control of the distribution and
sparsity of the sampled background scenes, and (iii) quality assurance using
visual representations of invariance testing results (referred to as variance
matrices). In this paper, we also report the training of an ML4ML assessor to
evaluate the invariance quality of ML models automatically.
