NDLI: Feature weighting methods for abstract features applicable to motion based video indexing

Content Provider	IEEE Xplore Digital Library
Author	Rahman, A. Murshed, M. Dooley, L.S.
Copyright Year	2004
Description	Author affiliation: Gippsland Sch. of Comp. & IT, Monash Univ., Melbourne, Vic., Australia (Rahman, A.; Murshed, M.; Dooley, L.S.)
Abstract	Content based labels, associated with image sequences in contemporary video indexing methods, can be textual, numerical as well as abstract, including colour-histograms and motion co-occurrence matrices. Abstract features or indices are not explicitly numeric entities but rather are composed of numeric entities. When multiple abstract features are involved, distance metrics between image sequences need to be weighted. Most feature weighting methods in the literature assume that the space is numeric (either discrete or continuous) and so not applicable to abstract feature weighting. This paper elaborates some feature weighting methods applicable to abstract features and both binary (feature selection) and real-valued weighting methods are discussed. The performance of different feature selection and weighting methods are provided and a comparative study based on motion classification-experiments is presented.
Sponsorship	IEEE Comput. Soc. Task Force on Information Technology for Business Application
Starting Page	676
Ending Page	680
File Size	1486791
Page Count	5
File Format	PDF
ISBN	0769521088
DOI	10.1109/ITCC.2004.1286544
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Indexing Feedback Video sequences Filters Image sequences Information retrieval Australia Multimedia computing Computer vision Biomedical imaging
Content Type	Text
Resource Type	Article

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