Semantic Tags for Lecture Videos

In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The keywords undergo a series of refinement process to select few but meaningful set of tags. We use word similarity measure using visual ness and word sense disambiguation to select the tags from candidate keywords. These tags are finally associated with video segments in which they appear based on timestamp. Each video segment represents a key idea or a topic. We also evaluated the objective keyword selection criteria to subjective test with some interesting results.