Algorithms for clustering data
Algorithms for clustering data
Multimedia information retrieval: what is it, and why isn't anyone using it?
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
MPEG-7 Compliant Shot Detection in Sport Videos
ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
PEANO: pictorial enriched annotation of video
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
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In this work, we analyze the effectiveness of perceptual features to automatically annotate video clips in domain-specific video digital libraries. Typically, automatic annotation is provided by computing clip similarity with respect to given examples, which constitute the knowledgebase, in accordance with a given ontology or a classification scheme. Since the amount of training clips is normally very large, we propose to automatically extract some prototypes, or visual concepts, for each class instead of using the whole knowledge base. The prototypes are generated after a Complete Link clustering based on perceptual features with an automatic selection of the number of clusters. Context based information are used in an intra-class clustering framework to provide selection of more discriminative clips. Reducing the number of samples makes the matching process faster and lessens the storage requirements. Clips are annotated following the MPEG-7 directives to provide easier portability. Results are provided on videos taken from sports and news digital libraries.