Abstract
This thesis studies the fundamental question: what vocabulary of concepts are suited for machines to describe video content? The answer to this question involves two annotation steps: First, to specify a list of concepts by which videos are described. Second, to label a set of videos per concept as its examples or counter examples. Subsequently, the vocabulary is constructed as a set of video concept detectors learned from the provided annotations by supervised learning.
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