%Aigaion2 BibTeX export from Idiap Publications
%Friday 11 October 2024 12:39:21 AM

@INPROCEEDINGS{gatica03b-conf,
         author = {Gatica-Perez, Daniel and Sun, Ming-Ting},
       projects = {Idiap},
          title = {Object Localization in Metric Spaces for Video Linking},
      booktitle = {IEEE Workshop on Motion and Video Computing},
           year = {2002},
       crossref = {gatica03b},
       abstract = {While objects often constitute the desired level of access for browsing and retrieval in video databases, an inherent problem for on-line object definition is that of model construction from a few examples. In this paper, we present a probabilistic methodology to localize objects that appear across video segments, based on video structuring, object definition, and localization in the video structure. Localization is formulated as a problem of random sampling in a Metric Mixture Model framework, which allows for the joint modeling of a set of color appearance exemplars and their geometric transformations. To improve the efficiency of the sampling process, candidate configurations are drawn from a prior distribution using importance sampling, and evaluated using Bayes' rule. Experimental results on a database extracted from home videos depicting real objects (with variations of scale and pose) across video shots show the performance of the method.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-09.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-09.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{gatica03b,
         author = {Gatica-Perez, Daniel and Sun, Ming-Ting},
       projects = {Idiap},
          title = {Object Localization in Metric Spaces for Video Linking},
           type = {Idiap-RR},
         number = {Idiap-RR-09-2003},
           year = {2003},
    institution = {IDIAP},
       abstract = {While objects often constitute the desired level of access for browsing and retrieval in video databases, an inherent problem for on-line object definition is that of model construction from a few examples. In this paper, we present a probabilistic methodology to localize objects that appear across video segments, based on video structuring, object definition, and localization in the video structure. Localization is formulated as a problem of random sampling in a Metric Mixture Model framework, which allows for the joint modeling of a set of color appearance exemplars and their geometric transformations. To improve the efficiency of the sampling process, candidate configurations are drawn from a prior distribution using importance sampling, and evaluated using Bayes' rule. Experimental results on a database extracted from home videos depicting real objects (with variations of scale and pose) across video shots show the performance of the method.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-09.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-09.ps.gz},
ipdmembership={vision},
}