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      • Department of Computer Engineering
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      Recognizing objects and scenes in news videos

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      Author
      Baştan, Muhammet
      Duygulu, Pınar
      Date
      2006-07
      Source Title
      5th International Conference on Image and Video Retrieval. CIVR 2006: Image and Video Retrieval
      Publisher
      Springer
      Pages
      380 - 390
      Language
      English
      Type
      Conference Paper
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      Abstract
      We propose a new approach to recognize objects and scenes in news videos motivated by the availability of large video collections. This approach considers the recognition problem as the translation of visual elements to words. The correspondences between visual elements and words are learned using the methods adapted from statistical machine translation and used to predict words for particular image regions (region naming), for entire images (auto-annotation), or to associate the automatically generated speech transcript text with the correct video frames (video alignment). Experimental results are presented on TRECVID 2004 data set, which consists of about 150 hours of news videos associated with manual annotations and speech transcript text. The results show that the retrieval performance can be improved by associating visual and textual elements. Also, extensive analysis of features are provided and a method to combine features are proposed. © Springer-Verlag Berlin Heidelberg 2006.
      Keywords
      Feature extraction
      Image analysis
      Multimedia systems
      Speech recognition
      Statistical methods
      News videos
      Statistical machine translation
      Video collections
      Video frames
      Object recognition
      Permalink
      http://hdl.handle.net/11693/27256
      Published Version (Please cite this version)
      https://doi.org/10.1007/11788034_39
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      • Department of Computer Engineering 1368
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