Efficient summarization of stereoscopic video sequences

Doulamis, ND; Doulamis, AD; Avrithis, YS; Ntalianis, KS; Kollias, SD

dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Avrithis, YS	en
dc.contributor.author	Ntalianis, KS	en
dc.contributor.author	Kollias, SD	en
dc.date.accessioned	2014-03-01T01:15:34Z
dc.date.available	2014-03-01T01:15:34Z
dc.date.issued	2000	en
dc.identifier.issn	1051-8215	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13606
dc.subject	content-based indexing and retrieval	en
dc.subject	stereoscopic image analysis	en
dc.subject	video summarization	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	IMAGE SEQUENCES	en
dc.subject.other	SEGMENTATION	en
dc.subject.other	TRACKING	en
dc.subject.other	OBJECTS	en
dc.subject.other	MODEL	en
dc.subject.other	REPRESENTATION	en
dc.subject.other	GENERATION	en
dc.subject.other	MESH	en
dc.title	Efficient summarization of stereoscopic video sequences	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/76.844996	en
heal.identifier.secondary	http://dx.doi.org/10.1109/76.844996	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	An efficient technique for summarization of stereoscopic video sequences is presented in this paper, which extracts a small but meaningful set of video frames using a content-based sampling algorithm. The proposed video-content representation provides the capability of browsing digital stereoscopic video sequences and performing more efficient content-based queries and indexing. Each stereoscopic video sequence is first partitioned into shots by applying a shot-cut detection algorithm so that frames (or stereo pairs) of similar visual characteristics are gathered together. Each shot is then analyzed using stereo-imaging techniques, and the disparity field, occluded areas, and depth map are estimated. A multiresolution implementation of the Recursive Shortest Spanning Tree (RSST) algorithm is applied for color and depth segmentation, while fusion of color and depth segments is employed for reliable video object extraction. In particular, color segments are projected onto depth segments so that video objects on the same depth plane are retained, while at the same time accurate object boundaries are extracted. Feature vectors are then constructed using multidimensional fuzzy classification of segment features including size, location, color, and depth. Shot selection is accomplished by clustering similar shots based on the generalized Lloyd-Max algorithm, while for a given shot, key frames are extracted using an optimization method for locating frames of minimally correlated feature vectors. For efficient implementation of the latter method, a genetic algorithm is used. Experimental results are presented, which indicate the reliable performance of the proposed scheme on real-life stereoscopic video sequences.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY	en
dc.identifier.doi	10.1109/76.844996	en
dc.identifier.isi	ISI:000087613700001	en
dc.identifier.volume	10	en
dc.identifier.issue	4	en
dc.identifier.spage	501	en
dc.identifier.epage	517	en