Titlebook: Video Mining; Azriel Rosenfeld,David Doermann,Daniel DeMenthon Book 2003 Springer Science+Business Media New York 2003 Audio.Frames.inform

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1571-5205 have obvi­ ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re­ searchers i978-1-4419-5383-4978-1-4757-6928-9Series ISSN 1571-5205

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Ajay Divakaran,Kadir A. Peker,Regunathan Radhakrishnan,Ziyou Xiong,Romain Cabasson

Temporal-Lobe

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Efficient Video Browsing,d on a screen. On the other hand, browsing of multiple audio and video documents could be very time-consuming. Even the task of browsing a single one-hour video to find a relevant segment might take considerable time. Different visualization methods have been developed over the years to assist video

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Beyond Key-Frames: The Physical Setting as a Video Mining Primitive,ual abstraction of semantic content. Our approach is based on building a highly compact hierarchical representation for long sequences. This is achieved by using non-temporal clustering of scene segments into a new conceptual form grounded in the recognition of real-world backgrounds. We represent s

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Video Summarization Using Mpeg-7 Motion Activity and Audio Descriptors,domain and is compact, and hence is easy to extract and match. We establish that the intensity of motion activity of a video shot is a direct indication of its summarizability. We describe video summarization techniques based on sampling in the cumulative motion activity space. We then describe comb

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