Robust image recognition by fusion of contextual information

This paper studies the fusion of contextual information in pattern recognition, with applications to biomedical image identification. In the real world there are cases where the identity of an object is ambiguous if the classification is based only on its own features. It is helpful to reduce the ambiguity by utilizing extra information, referred to as context, provided by accompanying objects. We investigate two techniques that incorporate context. The first approach, based on compound Bayesian theory, incorporates context by fusing the measurements of all objects under consideration. It is an optimal strategy in terms of achieving minimum set-by-set error probability. The second approach fuses the measurements of an object with explicitly extracted context. Its linear computational complexity makes it more tractable than the first approach, which requires exponential computation. These two techniques are applied to two medical applications: white blood cell image classification and microscopic urinalysis. It is demonstrated that superior classification performances are achieved by using context. In our particular applications, it reduces overall classification error, as well as false positive and false negative diagnosis rates.