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Adaptive robust clustering with proximity-based merging for video-summary

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3 Author(s)
Le Saux, B. ; Inst. Nat. de Recherche en Inf. et Autom., Le Chesnay, France ; Grira, N. ; Nozha Boujemaa

To allow efficient browsing of large image collection, we have to provide a summary of its visual content. We present in this paper a new robust approach to categorize image databases: Adaptive Robust Competition with Proximity-Based Merging (ARC-M). This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. Each image is represented by a high-dimensional vector in the feature space. A principal component analysis is performed for every feature to reduce dimensionality. Then, clustering is performed in challenging conditions by minimizing a Competitive Agglomeration objective function with an extra noise cluster to collect outliers. Agglomeration is improved by a merging process based on cluster proximity verification.

Published in:

Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on  (Volume:2 )

Date of Conference:

25-28 May 2003