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We propose a fully automatic method for summarizing and indexing unstructured presentation videos based on text extracted from the projected slides. We use changes of text in the slides as a means to segment the video into semantic shots. Unlike precedent approaches, our method does not depend on availability of the electronic source of the slides, but rather extracts and recognizes the text directly...
With fast paced growth of digital data, keyword based search has become a critical enterprise application. Research has shown that nearly 85% of enterprise data lies in flat filesystems [10] that allow multiple users with different access privileges. Any search tool for such systems needs to be efficient and yet
Information overload occurs when the information available exceeds the user's ability to process it. To manage information overload, a user is required to discriminate among useful, redundant, incorrect, and meaningless information. From a computer science perspective, this means we must provide users with a combination of techniques and tools for collecting, grouping, classifying, selecting, indexing,...
of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and
Tagging with free form tags is becoming an increasingly important indexing mechanism. However, free form tags have characteristics that require special treatment when used for searching or recommendation because they show much more variation than controlled keywords. In this paper we present a method that puts this
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