Finding structure in home videos by probabilistic hierarchical clustering

@article{citeulike:1319154, abstract = {Accessing, organizing, and manipulating home videos present technical challenges due to their unrestricted content and lack of storyline. 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: (1) the development of statistical models of visual similarity, duration, and temporal adjacency of consumer video segments and (2) 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 intra- and inter-segment visual and temporal features. 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 10-h 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.}, author = {Gatica-Perez, D. and Loui, A. and Sun, Ming-Ting }, citeulike-article-id = {1319154}, journal = {Circuits and Systems for Video Technology, IEEE Transactions on}, keywords = {analysis, home, memoir, video}, number = {6}, pages = {539--548}, posted-at = {2007-05-22 11:38:08}, priority = {0}, title = {Finding structure in home videos by probabilistic hierarchical clustering}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1207412}, volume = {13}, year = {2003} }

TY - JOUR ID - citeulike:1319154 L3 - citeulike-article-id:1319154 TI - Finding structure in home videos by probabilistic hierarchical clustering JF - Circuits and Systems for Video Technology, IEEE Transactions on VL - 13 IS - 6 SP - 539 EP - 548 N2 - Accessing, organizing, and manipulating home videos present technical challenges due to their unrestricted content and lack of storyline. 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: (1) the development of statistical models of visual similarity, duration, and temporal adjacency of consumer video segments and (2) 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 intra- and inter-segment visual and temporal features. 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 10-h 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. KW - analysis KW - home KW - memoir KW - video AU - Gatica-Perez, D AU - Loui, A AU - Sun, Ming-Ting PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1207412 ER -