Non-sequential video content representation using temporal variation of feature vectors

Doulamis, AD; Doulamis, N; Kollias, S

dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Doulamis, N	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:15:44Z
dc.date.available	2014-03-01T01:15:44Z
dc.date.issued	2000	en
dc.identifier.issn	0098-3063	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13698
dc.subject	video content analysis	en
dc.subject	vide summarization	en
dc.subject	feature extraction	en
dc.subject	MPEG sequences	en
dc.subject	fuzzy classification	en
dc.subject	and temporal frame variation	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Telecommunications	en
dc.subject.other	IMAGE	en
dc.title	Non-sequential video content representation using temporal variation of feature vectors	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/30.883444	en
heal.identifier.secondary	http://dx.doi.org/10.1109/30.883444	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	In this paper, an efficient and low complexity algorithm for non-sequential video content representation is proposed. Our method is based on extracting a set of limited but meaningful frames (key-frames), able to represent the video content, The temporal variation of feature vectors for all frames within a shot, which form a trajectory in a high dimensional space, is used for key-frame selection, In particular, key-frames are extracted by estimating appropriate curve points, able to characterize the feature trajectory. The magnitude of the second derivative of the frame feature vectors with respect to time is used as a curvature measure in our approach. Due to low complexity of the algorithm, the proposed method can be easily implemented in hardware devices of even low processing capabilities thus can be embedded in many consumer electronics systems. For feature vector formulation, the video is first analyzed and several descriptors are extracted using a multiscale implementation of the Recursive Shortest Spanning Tree (RSST) algorithm, which significantly reduces the segmentation complexity. In addition, the whole procedure exploits information that exists in MPEG video databases so as to achieve a faster implementation. Finally, the extracted descriptors are classified using a fuzzy formulation scheme. Experimental results to real-life video sequences are presented to indicate the good performance of the proposed algorithm.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE TRANSACTIONS ON CONSUMER ELECTRONICS	en
dc.identifier.doi	10.1109/30.883444	en
dc.identifier.isi	ISI:000089667900047	en
dc.identifier.volume	46	en
dc.identifier.issue	3	en
dc.identifier.spage	758	en
dc.identifier.epage	768	en