Neighbor Embedding with Non-negative Matrix Factorization for image prediction

Christine Guillemot 1 Mehmet Turkan 2
1 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The paper studies several non-negative matrix factorization methods with nearest neighbors constrained dictionaries for image prediction. The methods considered include the multiplicative update algorithm, the projected gradient algorithm, as well as the graph-regularized NMF solution which aims at taking into account the geometrical structure of the input data. The Intra prediction problem based on these NMF solutions amounts to a neighbor embedding problem. Both prediction and rate-distortion performances are then given in comparison with other neighbor embedding methods like locally linear embedding (LLE) and locally linear embedding with low dimensional neigborhood representation (LLE-LDNR).
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, Mar 2012, Kyoto, Japan. pp.785-788, 2012, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6288001&tag=1〉
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https://hal.inria.fr/hal-00755720
Contributeur : Christine Guillemot <>
Soumis le : mercredi 21 novembre 2012 - 17:43:56
Dernière modification le : mercredi 16 mai 2018 - 11:23:38

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  • HAL Id : hal-00755720, version 1

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Christine Guillemot, Mehmet Turkan. Neighbor Embedding with Non-negative Matrix Factorization for image prediction. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, Mar 2012, Kyoto, Japan. pp.785-788, 2012, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6288001&tag=1〉. 〈hal-00755720〉

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