TY - JOUR
T1 - A scalable summary generation method based on cross-modal consensus clustering and OLAP cube modeling
AU - Sargent, Gabriel
AU - Perez-Daniel, Karina R.
AU - Stoian, Andrei
AU - Benois-Pineau, Jenny
AU - Maabout, Sofian
AU - Nicolas, Henri
AU - Miyatake, Mariko Nakano
AU - Carrive, Jean
N1 - Publisher Copyright:
© Springer Science+Business Media New York 2015
PY - 2015/9/1
Y1 - 2015/9/1
N2 - Video summarization has been a core problem to manage the growing amount of content in multimedia databases. An efficient video summary should display an overview of the video content and most existing approaches fulfill this goal. However, such an overview does not allow the user to reach all details of interest selectively and progressively. This paper proposes a novel scalable summary generation approach based on the On-Line Analytical Processing data cube. Such a structure integrates tools like the drill down operation allowing to browse efficiently multiple descriptions of a dataset according to increased levels of detail. We adapt this model to video summary generation by expressing a video within a cross-media feature space and by performing clusterings according to particular subspaces. Consensus clustering is used to guide the subspace selection strategy at small dimensions, as the novelty brought by the least consensual subspaces is interesting for the refinements of a summary. Our approach is designed for weakly-structured contents such as cultural documentaries. We perform its evaluation on a corpus of cultural archives provided by the French Audiovisual National Institute (INA) using information retrieval metrics handling single and multiple reference annotations. The performances obtained overall improved results compared to two baseline systems performing random and arbitrary segmentations, showing a better balance between Precision and Recall.
AB - Video summarization has been a core problem to manage the growing amount of content in multimedia databases. An efficient video summary should display an overview of the video content and most existing approaches fulfill this goal. However, such an overview does not allow the user to reach all details of interest selectively and progressively. This paper proposes a novel scalable summary generation approach based on the On-Line Analytical Processing data cube. Such a structure integrates tools like the drill down operation allowing to browse efficiently multiple descriptions of a dataset according to increased levels of detail. We adapt this model to video summary generation by expressing a video within a cross-media feature space and by performing clusterings according to particular subspaces. Consensus clustering is used to guide the subspace selection strategy at small dimensions, as the novelty brought by the least consensual subspaces is interesting for the refinements of a summary. Our approach is designed for weakly-structured contents such as cultural documentaries. We perform its evaluation on a corpus of cultural archives provided by the French Audiovisual National Institute (INA) using information retrieval metrics handling single and multiple reference annotations. The performances obtained overall improved results compared to two baseline systems performing random and arbitrary segmentations, showing a better balance between Precision and Recall.
KW - Consensus clustering
KW - Cross-media space
KW - Data cube
KW - Drill down
KW - Scalability
KW - Video summarization
UR - http://www.scopus.com/inward/record.url?scp=84988384737&partnerID=8YFLogxK
U2 - 10.1007/s11042-015-2863-3
DO - 10.1007/s11042-015-2863-3
M3 - Artículo
SN - 1380-7501
VL - 75
SP - 9073
EP - 9094
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 15
ER -