Structuring home video by snippet detection and pattern parsing
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Semantic-event based analysis and segmentation of wedding ceremony videos
Proceedings of the international workshop on Workshop on multimedia information retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval
Computer Vision and Image Understanding
An overview of video shot clustering and summarization techniques for mobile applications
MobiMedia '06 Proceedings of the 2nd international conference on Mobile multimedia communications
Using cross-media correlation for scene detection in travel videos
Proceedings of the ACM International Conference on Image and Video Retrieval
Spectral structuring of home videos
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
A user-centric system for home movie summarisation
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Linking identities and viewpoints in home movies based on robust feature matching
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Travel photo and video summarization with cross-media correlation and mutual influence
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification
International Journal of Multimedia Data Engineering & Management
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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.