%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{gatica02c-art,
         author = {Gatica-Perez, Daniel and Loui, Alexander and Sun, Ming-Ting},
       projects = {Idiap},
          title = {Finding Structure in Home Videos by Probabilistic Hierarchical Clustering},
        journal = {IEEE Transactions on Circuits and Systems for Video Technology},
         volume = {13},
         number = {6},
           year = {2003},
           note = {IDIAP-RR 02-22},
       crossref = {gatica02c},
       abstract = {Accessing, organizing, and manipulating home videos present technical challenges due to their unrestricted content and lack of storyline. In this paper, we present a methodology to discover cluster structure in home videos, which uses video shots as the unit of organization, and is based on two concepts: (i) the development of statistical models of visual similarity and temporal duration and adjacency of consumer video segments, and (ii) the reformulation of hierarchical clustering as a sequential binary Bayesian classification process. A Bayesian formulation allows for the incorporation of prior knowledge of the structure of home video, and offers the advantages of a principled methodology. Gaussian mixture models are used to represent the class-conditional distributions of inter-segment visual similarity, and temporal adjacency and duration. The models are then used in the probabilistic clustering algorithm, where the merging order is a variation of Highest Confidence First, and the merging criterion is Maximum a Posteriori. The algorithm does not need any ad-hoc parameter determination. We present extensive results on a ten-hour home video database with ground-truth which thoroughly validate the performance of our methodology with respect to cluster detection, individual shot-cluster labeling, and the effect of prior selection.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-22.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-22.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{gatica02c,
         author = {Gatica-Perez, Daniel and Loui, Alexander and Sun, Ming-Ting},
       projects = {Idiap},
          title = {Finding Structure in Consumer Videos by Probabilistic Hierarchical Clustering},
           type = {Idiap-RR},
         number = {Idiap-RR-22-2002},
           year = {2002},
    institution = {IDIAP},
           note = {IEEE Transactions on Circuits and Systems for Video Technology, accepted for publication},
       abstract = {Accessing, organizing, and manipulating home videos present technical challenges due to their unrestricted content and lack of storyline. In this paper, we present a methodology to discover cluster structure in home videos, which uses video shots as the unit of organization, and is based on two concepts: (i) the development of statistical models of visual similarity and temporal duration and adjacency of consumer video segments, and (ii) the reformulation of hierarchical clustering as a sequential binary Bayesian classification process. A Bayesian formulation allows for the incorporation of prior knowledge of the structure of home video, and offers the advantages of a principled methodology. Gaussian mixture models are used to represent the class-conditional distributions of inter-segment visual similarity, and temporal adjacency and duration. The models are then used in the probabilistic clustering algorithm, where the merging order is a variation of Highest Confidence First, and the merging criterion is Maximum a Posteriori. The algorithm does not need any ad-hoc parameter determination. We present extensive results on a ten-hour home video database with ground-truth which thoroughly validate the performance of our methodology with respect to cluster detection, individual shot-cluster labeling, and the effect of prior selection.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-22.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-22.ps.gz},
ipdmembership={vision},
}