학술논문
An EM algorithm for video summarization, generative model approach
Document Type
Conference
Author
Source
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 Computer vision Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on. 2:335-342 vol.2 2001
Subject
Language
Abstract
In this paper, we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence. Given a set of time correlated frames, we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution. To this end, we present a generative model which involves jointly the representation and the evolution of appearance. Applying Linear Dynamical System theory to this problem, we discuss how the temporal information is encoded yielding a manner of grouping the iconic representations of the video sequence in terms of invariance. The formulation of this problem is driven in terms of a probabilistic approach, which affords a measure of perceptual similarity taking both learned appearance and time evolution models into account.