NDLI: Separability and refinement of hierarchical semantic video labels and their ground truth

Content Provider	IEEE Xplore Digital Library
Author	Kender, J.R.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Columbia Univ., New York, NY (Kender, J.R.)
Abstract	We investigate several problems in the annotation of video shots by semantic labels which are implicitly embedded in a semantic hierarchy, leading to analyses and novel methods for refining video ontologies and their ground truth. First, in the large 449 LSCOM semantic concept data set, we show that within the implicit ldquouse ontologyrdquo, many concepts tags are ambiguous as to purposeful activity, visual scope, or social agency, or are absent altogether, but that better ldquouse sensesrdquo can be refined algorithmically. Second, we find that both traditional hard and fuzzy k-medoid clustering techniques are inadequate for hierarchical concepts, but a novel ldquofirm k-medoidrdquo clustering method both separates clusters and distributes superconcepts equitably. Third, we show how the scores of SVM semantic filters can be more reliably and quickly converted to probabilities by using a closed-form approximation to SVM behavior between its margins. Fourth, we show that the quality of SVM semantic filters for hierarchical concepts can be analyzed by their ability to separate their positive ground truth examples from those of any other concept in the hierarchy; the most discriminating are those with ground truth showing distinctive physical backgrounds.
Starting Page	673
Ending Page	676
File Size	186055
Page Count	4
File Format	PDF
ISBN	9781424425709
DOI	10.1109/ICME.2008.4607524
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Ontologies Distance measurement Training Clustering algorithms Visualization Materials
Content Type	Text
Resource Type	Article

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