TY - JOUR
T1 - Spatiotemporal Saliency Estimation by Spectral Foreground Detection
AU - Aytekin, Çaglar
AU - Possegger, Horst
AU - Mauthner, Thomas
AU - Kiranyaz, Serkan
AU - Bischof, Horst
AU - Gabbouj, Moncef
PY - 2018
Y1 - 2018
N2 - We present a novel approach for spatiotemporal saliency detection by optimizing a unified criterion of color contrast, motion contrast, appearance, and background cues. To this end, we first abstract the video by temporal superpixels. Second, we propose a novel graph structure exploiting the saliency cues to assign the edge weights. The salient segments are then extracted by applying a spectral foreground detection method, quantum cuts, on this graph. We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art.
AB - We present a novel approach for spatiotemporal saliency detection by optimizing a unified criterion of color contrast, motion contrast, appearance, and background cues. To this end, we first abstract the video by temporal superpixels. Second, we propose a novel graph structure exploiting the saliency cues to assign the edge weights. The salient segments are then extracted by applying a spectral foreground detection method, quantum cuts, on this graph. We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art.
U2 - 10.1109/TMM.2017.2713982
DO - 10.1109/TMM.2017.2713982
M3 - Article
SN - 1520-9210
VL - 20
SP - 82
EP - 95
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
IS - 1
ER -