TY - JOUR

T1 - Edited nearest neighbour for selecting keyframe summaries of egocentric videos

AU - Kuncheva, Ludmila

AU - Yousefi, Paria

AU - Almeida, Jurandy

PY - 2018/4

Y1 - 2018/4

N2 - A keyframe summary of a video must be concise, comprehensive and diverse. Current video summarisation methods may not be able to enforce diversity of the summary if the events have highly similar visual content, as is the case of egocentric videos. We cast the problem of selecting a keyframe summary as a problem of prototype (instance) selection for the nearest neighbour classifier (1 nn). Assuming that the video is already segmented into events of interest (classes), and represented as a dataset in some feature space, we propose a Greedy Tabu Selector algorithm (GTS) which picks one frame to represent each class. An experiment with the UT (Egocentric) video database and seven feature representations illustrates the proposed keyframe summarisation method. GTS leads to improved match to the user ground truth compared to the closest-to centroid baseline summarisation method. Best results were obtained with feature spaces obtained from a convolutional neural network (CNN).

AB - A keyframe summary of a video must be concise, comprehensive and diverse. Current video summarisation methods may not be able to enforce diversity of the summary if the events have highly similar visual content, as is the case of egocentric videos. We cast the problem of selecting a keyframe summary as a problem of prototype (instance) selection for the nearest neighbour classifier (1 nn). Assuming that the video is already segmented into events of interest (classes), and represented as a dataset in some feature space, we propose a Greedy Tabu Selector algorithm (GTS) which picks one frame to represent each class. An experiment with the UT (Egocentric) video database and seven feature representations illustrates the proposed keyframe summarisation method. GTS leads to improved match to the user ground truth compared to the closest-to centroid baseline summarisation method. Best results were obtained with feature spaces obtained from a convolutional neural network (CNN).

U2 - 10.1016/j.jvcir.2018.02.010

DO - 10.1016/j.jvcir.2018.02.010

M3 - Article

VL - 52

SP - 118

EP - 130

JO - Journal of Visual Communication and Image Representation

JF - Journal of Visual Communication and Image Representation

SN - 1047-3203

ER -