CONF gatica02a-conf/IDIAP Probabilistic Home Video Structuring: Feature Selection and Performance Evaluation Gatica-Perez, Daniel Sun, Ming-Ting Loui, Alexander EXTERNAL https://publications.idiap.ch/attachments/reports/2002/rr02-11.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/gatica02a Related documents IEEE International Conference on Image Processing 2002 We recently proposed a method to find cluster structure in home videos based on statistical models of visual and temporal features of video segments and sequential binary Bayesian classification. In this paper, we present analysis and improved results on two key issues: feature selection and performance evaluation, using a ten-hour database (30 video clips, 1,075,000 frames). From multiple features and similarity measures, visual features are selected in order to minimize the empirical probability of misclassification. Temporal features are chosen to reflect the patterns existing in both shot and cluster duration and adjacency. Finally, we describe a detailed performance evaluation procedure that includes cluster detection, individual shot-cluster labeling, and prior selection. REPORT gatica02a/IDIAP Probabilistic Home Video Structuring: Feature Selection and Performance Evaluation Gatica-Perez, Daniel Sun, Ming-Ting Loui, Alexander EXTERNAL https://publications.idiap.ch/attachments/reports/2002/rr02-11.pdf PUBLIC Idiap-RR-11-2002 2002 IDIAP We recently proposed a method to find cluster structure in home videos based on statistical models of visual and temporal features of video segments and sequential binary Bayesian classification. In this paper, we present analysis and improved results on two key issues: feature selection and performance evaluation, using a ten-hour database (30 video clips, 1,075,000 frames). From multiple features and similarity measures, visual features are selected in order to minimize the empirical probability of misclassification. Temporal features are chosen to reflect the patterns existing in both shot and cluster duration and adjacency. Finally, we describe a detailed performance evaluation procedure that includes cluster detection, individual shot-cluster labeling, and prior selection.