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The Next Generation of Video Indexing
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The next generation of video indexing technology will build on the observational modalities of present systems to provide higher-level semantic information and structure, resulting in a more useful and powerful video index The primary goal of current research is to automatically determine meaningful boundaries within the video, wherein the subject matter of the video is coherent This process is known as video segmentation and results in a higher-level structure of clips (defined by in- and out-points within a larger piece of video), thereby providing richer opportunities for search, retrieval, classification, and summarization of video content Currently, human beings mainly perform video segmentation in commercial applications by interacting with a video indexing system to mark the clip boundaries and assign categories and summaries in a manual process The next generation will largely automate this process, as well as produce additional value, with such features as summarization, topic identification, and proper noun identification Virage long ago anticipated the emergence of second-order analysis techniques and pioneered a framework capable of supporting the multimodal fusion approach to analysis Not only can media analysis plug-ins subscribe to raw signals (pixels, audio samples), but they also can access any metadata track generated by other plug-ins Moreover, groups of plug-ins can cooperate and exchange information with human beings, providing the framework for the next generation of video indexing
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Virage s VideoLogger has a rich, open, and extensible architecture supported by a robust software development kit (SDK) One of the central interfaces in this architecture is the media analysis framework
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In the past few years, a number of new technologies from leading-edge companies, academic research institutions, and government-sponsored research programs have reached a level of maturity that will allow them to become commercially viable in the near future Examples include various forms of linguistic analysis (in English and other languages) that can identify and track topics, produce summaries of news stories, identify proper nouns and their type (person, place, organization), and place content segments into one or more categories for keyword retrieval These types of analysis are second-order, in that they operate on metadata generated by the first-order observational and signalanalysis techniques By themselves, second-order technologies can offer real value by enriching the video index and producing results that formerly required human intervention The real breakthrough for next-generation indexing, however, comes about through the coordinated and intelligent use of several first- and second-order technologies This approach is known as multimode fusion By using information from several observations simultaneously and taking hints from the second-order language-analysis techniques, we can achieve the goal of intelligent video segmentation The ability to automatically chop video content up into semantically coherent segments is of immense value in video production, monitoring, and archive environments The meaning of semantically coherent segments for video varies greatly across domains For example:
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For news, the goal is to segment a newscast into individual stories or sections such as weather, finance, sports, and so on For entertainment content (such as a movie or a soap opera), the segmentation should map to the scene structure
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Another way of looking at this is to explore the traditional method broadcasters use to present their content If you were to view a length of videotape representing just the content for a full-length news or entertainment segment, you would observe occasional black frames and an audio dropout, indicating the space needed for a commercial break
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Various metadata standards are gaining momentum concurrent with advances in indexing technology MPEG-7, AAF, RDF, the Dublin Core, NewsML, and SMPTE standards all address various aspects of content description Metadata authoring will rapidly become a core function in all media assembly and production processes
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Each domain of content, therefore, has its own unique structure and corresponding cues that indicate segment boundaries As a result, there is no one single algorithm that can intelligently segment all video The key to effective use of the multimodal fusion approach is to: 1 Build probabilistic models for state transitions within the video that can be trained and tailored for the various content domains 2 Serve each domain (for example, news or entertainment) by a specific set of observations mapped onto a specific state-transition model 3 Then train the model against a known corpus of ground truth evaluated, and tuned for optimal performance Much of these metadata will be derived from automatic indexing functions As the standards find their niche in the content value chain, the need for more and better metadata, requiring little or no human intervention, becomes paramount The timing is perfect for secondgeneration indexing to meld with metadata authoring in accordance with new standards
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