Abstract
We present a new measure for evaluation of algorithms for the detection of regions of interest (ROI) in, e.g., attention mechanisms. In contrast to existing measures, the present approach handles situations of order uncertainties, where the order for some ROIs is crucial, while for others it is not. We compare the results of several measures in some theoretical cases as well as some real applications. We further demonstrate how our measure can be used to evaluate algorithms for ROI detection, particularly the model of Itti and Koch for bottom-up data-driven attention.
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© 2004 Springer-Verlag Berlin Heidelberg
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Clauss, M., Bayerl, P., Neumann, H. (2004). A Statistical Measure for Evaluating Regions-of-Interest Based Attention Algorithms. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_47
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DOI: https://doi.org/10.1007/978-3-540-28649-3_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
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