CONF gatica03b-conf/IDIAP Object Localization in Metric Spaces for Video Linking Gatica-Perez, Daniel Sun, Ming-Ting EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-09.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/gatica03b Related documents IEEE Workshop on Motion and Video Computing 2002 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. REPORT gatica03b/IDIAP Object Localization in Metric Spaces for Video Linking Gatica-Perez, Daniel Sun, Ming-Ting EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-09.pdf PUBLIC Idiap-RR-09-2003 2003 IDIAP 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.