Recognizing objects and scenes in news videos
Author
Baştan, Muhammet
Duygulu, Pınar
Date
2006-07Source Title
5th International Conference on Image and Video Retrieval. CIVR 2006: Image and Video Retrieval
Publisher
Springer
Pages
380 - 390
Language
English
Type
Conference PaperItem Usage Stats
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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 extractionImage analysis
Multimedia systems
Speech recognition
Statistical methods
News videos
Statistical machine translation
Video collections
Video frames
Object recognition