Enhancing temporal segmentation by nonlocal self-similarity
Document typeConference report
Rights accessOpen Access
Temporal segmentation of untrimmed videos and photo-streams is currently an active area of research in computer vision and image processing. This paper proposes a new approach to improve the temporal segmentation of photo-streams. The method consists in enhancing image representations by encoding long-range temporal dependencies. Our key contribution is to take advantage of the temporal stationarity assumption of photostreams for modeling each frame by its nonlocal self-similarity function. The proposed approach is put to test on the EDUB-Seg dataset, a standard benchmark for egocentric photostream temporal segmentation. Starting from seven different (CNN based) image features, the method yields consistent improvements in event segmentation quality, leading to an average increase of F-measure of 3.71% with respect to the state of the art.
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CitationDimiccoli, M.; Wendt, H. Enhancing temporal segmentation by nonlocal self-similarity. A: IEEE International Conference on Image Processing. "ICIP 26th International Conference on Image Processing". 2019, p. 3681-3685.