RT Conference_Proceedings
T1 Decorrelation and distinctiveness provide with human-like saliency
A1 García Díaz, Antón
A1 Fernández Vidal, Xosé Ramón
A1 Pardo López, Xosé Manuel
A1 Dosil Lago, Raquel
K1 Decorrelation
K1 Distinctiveness
AB In this work, we show the capability of a new model of saliency, of reproducing remarkable psychophysical results. The model presents low computational complexity compared to other models of the state of the art. It is based in biologically plausible mechanisms: the decorrelation and the distinctiveness of local responses. Decorrelation of scales is obtained from principal component analysis of multiscale low level features. Distinctiveness is measured through the Hotelling’s T2 statistic. The model is conceived to be used in a machine vision system, in which attention would contribute to enhance performance together with other visual functions. Experiments demonstrate the consistency with a wide variety of psychophysical phenomena, that are referenced in the visual attention modeling literature, with results that outperform other state of the art models.
PB Springer
YR 2009
FD 2009
LK http://hdl.handle.net/10347/32755
UL http://hdl.handle.net/10347/32755
LA eng
NO Garcia-Diaz, A., Fdez-Vidal, X.R., Pardo, X.M., Dosil, R. (2009). Decorrelation and Distinctiveness Provide with Human-Like Saliency. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg
NO This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-642-04697-1_32
NO This work has been granted by the Spanish Government (TIN2006-08447),and by the Government of Galicia (PGIDIT07PXIB206028PR).
DS Minerva
RD 24 abr 2026