TY - GEN
T1 - Proto-object based visual saliency model with a motion-sensitive channel
AU - Molin, Jamal Lottier
AU - Russell, Alexander F.
AU - Mihalas, Stefan
AU - Niebur, Ernst
AU - Etienne-Cummings, Ralph
PY - 2013/12/1
Y1 - 2013/12/1
N2 - The human visual system has the inherent capability of using selective attention to rapidly process visual information across visual scenes. Early models of visual saliency are purely feature-based and compute visual attention for static scenes. However, to model the human visual system, it is important to also consider temporal change that may exist within the scene when computing visual saliency. We present a biologically-plausible model of dynamic visual attention that computes saliency as a function of proto-objects modulated by an independent motion-sensitive channel. This motion-sensitive channel extracts motion information via biologically plausible temporal filters modeling simple cell receptive fields. By using KL divergence measurements, we show that this model performs significantly better than chance in predicting eye fixations. Furthermore, in our experiments, this model outperforms the Itti, 2005 dynamic saliency model and insignificantly differs from the graph-based visual dynamic saliency model in performance.
AB - The human visual system has the inherent capability of using selective attention to rapidly process visual information across visual scenes. Early models of visual saliency are purely feature-based and compute visual attention for static scenes. However, to model the human visual system, it is important to also consider temporal change that may exist within the scene when computing visual saliency. We present a biologically-plausible model of dynamic visual attention that computes saliency as a function of proto-objects modulated by an independent motion-sensitive channel. This motion-sensitive channel extracts motion information via biologically plausible temporal filters modeling simple cell receptive fields. By using KL divergence measurements, we show that this model performs significantly better than chance in predicting eye fixations. Furthermore, in our experiments, this model outperforms the Itti, 2005 dynamic saliency model and insignificantly differs from the graph-based visual dynamic saliency model in performance.
UR - http://www.scopus.com/inward/record.url?scp=84893578802&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893578802&partnerID=8YFLogxK
U2 - 10.1109/BioCAS.2013.6679631
DO - 10.1109/BioCAS.2013.6679631
M3 - Conference contribution
AN - SCOPUS:84893578802
SN - 9781479914715
T3 - 2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013
SP - 25
EP - 28
BT - 2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013
T2 - 2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013
Y2 - 31 October 2013 through 2 November 2013
ER -