TY - GEN
T1 - SEMBED: Semantic Embedding of Egocentric Action Videos
AU - Wray, Michael
AU - Moltisanti, Davide
AU - Mayol-Cuevas, Walterio
AU - Damen, Dima
PY - 2016/9/18
Y1 - 2016/9/18
N2 - We present SEMBED, an approach for embedding an egocentric object interaction video in a semantic-visual graph to estimate the probability distribution over its potential semantic labels. When object interactions are annotated using unbounded choice of verbs, we embrace the wealth and ambiguity of these labels by capturing the semantic relationships as well as the visual similarities over motion and appearance features. We show how SEMBED can interpret a challenging dataset of 1225 freely annotated egocentric videos, outperforming SVM classification by more than 5 %.
AB - We present SEMBED, an approach for embedding an egocentric object interaction video in a semantic-visual graph to estimate the probability distribution over its potential semantic labels. When object interactions are annotated using unbounded choice of verbs, we embrace the wealth and ambiguity of these labels by capturing the semantic relationships as well as the visual similarities over motion and appearance features. We show how SEMBED can interpret a challenging dataset of 1225 freely annotated egocentric videos, outperforming SVM classification by more than 5 %.
U2 - 10.1007/978-3-319-46604-0_38
DO - 10.1007/978-3-319-46604-0_38
M3 - Chapter in a published conference proceeding
SP - 532
EP - 545
BT - European Conference onf Computer Vision - Workshops
ER -