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Abstract:
In this paper we propose a technique to incorporate
contextual information into object classification. In the real
world there are cases where the identity of an object is ambiguous
due to the noise in the measurements based on which the
classification should be made. It is helpful to reduce the
ambiguity by utilizing extra information referred to as context,
which in our case is the identities of the accompanying objects.
This technique is applied to white blood cell classification.
Comparisons are made against the "no context" approach, to
demonstrate the superior classification performance achieved by
using context. In our particular application, it significantly
reduces false alarm rate and thus greatly reduces the cost due to
expensive clinical tests.
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