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Effective video retrieval is the result of interplay between interactive query selection, advanced visualization of results, and a goal-oriented human user. Traditional interactive video retrieval approaches emphasize paradigms, such as query-by-keyword and query-by-example, to aid the user in the search for relevant
models such as a vector space model, a language model and two probabilistic models. We also proposed different measures to compute textual entailment between two terms allowing us to hopefully select appropriate keywords from thesauri to expand documents or queries automatically.
analysis and processing algorithms to keywords and human annotation. We use the well known \FLICK\ system, that contains images, tags, keywords and sometimes useful annotation describing both the content of an image and personal interesting information describing the scene. We have carried out several experiments
keywords, plain text, voice, or a video clip, the query is parsed and the closest events are extracted utilizing text description and sub-graph matching.
With the increasing popularity of Web 2.0, a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.