Abstract
This paper presents a rationale for the design of optimal sequential sampling procedures for multi-hypothesis discrimination where a system selectively queries the environment based on the current state of the discrimination process.
The environment is modelled as a controlled i.i.d. process conditionned by various hypotheses. Recognition is achieved when the test identifies the correct hypothesis describing the environment behavior.
As the testing proceeds, hypotheses may be rejected with infinite confidence when feature values with zero probability are observed. The sampling strategy is stationary but is updated each time a hypothesis is rejected. It is chosen according to a criterion measuring the recognition error speed of convergence to zero when the number of samples goes to infinity. This criterion is obtained by Large Deviation Theory techniques and characterizes globally the multi-hypothesis discrimination problem. An application on 2D rotation invariant shape recognition with non-closed noisy contours illustrates the approach.
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Herbin, S. (2003). Active Sampling Strategies for Multihypothesis Testing. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_7
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DOI: https://doi.org/10.1007/978-3-540-45063-4_7
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