Event induced bias in label fusion

Christine M. Schubert Kabban; Alexander M. Venzin; Mark E. Oxley

doi:10.1117/12.2264632

2 May 2017 Event induced bias in label fusion

Christine M. Schubert Kabban, Alexander M. Venzin, Mark E. Oxley

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 102000G (2017) https://doi.org/10.1117/12.2264632
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

In a two class label scenario, classication systems may be used to assess whether or not an element of interest belongs to the targetor non-targetclass. The performance of the system is summarized visually as a trade- o¤ between the proportions of elements correctly labeled as targetplotted against the proportion of elements incorrectly labeled as target. These proportions are empirical estimates of the true and false positive rates, and their trade-o¤ plot is known as a receiver operating characteristic (ROC) curve. Classication performance can be increased, however, if the information provided by multiple systems can be fused together to create a new, combined system. This research focuses on label-fusion as a common method to increase classication performance and quantifying the bias that occurs when misspecifying the partitioning of the underlying event set. This partitioning will be dened in terms of what be called within and across label fusion. When incorrect assumptions are made about the partitioning of the event set, bias will occur and both the ROC curve and its optimal parameters will be incorrectly quantied. In this work, we analyze the e¤ects of individual classication system performance, correlation, and target environment on the magnitude of this performance bias. This work will then inspire the development of formulas to adjust optimal performance measures to appropriately reect the fused system performance according to event set partitioning. As such, bias may be appropriately adjusted without redesigning the fused system, allowing greater use of currently fused systems across multiple platforms and environments.

Citation Download Citation

Christine M. Schubert Kabban, Alexander M. Venzin, and Mark E. Oxley "Event induced bias in label fusion", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000G (2 May 2017); https://doi.org/10.1117/12.2264632

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KEYWORDS

Field emission displays

Fourier transforms

Target detection

Computing systems

Environmental sensing

Barium

Data processing

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