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      Haber videolarında nesne tanıma ve otomatik etiketleme

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      Author(s)
      Baştan, Muhammet
      Duygulu, Pınar
      Date
      2006-04
      Source Title
      2006 IEEE 14th Signal Processing and Communications Applications Conference
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      We propose a new approach to object recognition problem motivated by the availability of large annotated image and video collections. Similar to translation from one language to another, this approach considers the object recognition problem as the translation of visual elements to words. The visual elements represented in feature space are first categorized into a finite set of blobs. Then, the correspondences between the blobs and the words are learned using a method adapted from Statistical Machine Translation. Finally, the correspondences, in the form of a probability table, are 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. © 2006 IEEE.
      Keywords
      Auto annotation
      Statistical Machine Translation
      Video alignment
      Video frames
      Computational methods
      Image coding
      Multimedia services
      Translation (languages)
      Video streaming
      Word processing
      Object recognition
      Permalink
      http://hdl.handle.net/11693/27184
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2006.1659821
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      • Department of Computer Engineering 1435
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