Incremental Multi-Classifier Learning Algorithm on Grid'5000 for Large Scale Image Annotation

Bahjat Safadi 1 Yubing Tong 1 Georges Quénot 1
1 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique
Abstract : With our previous research, active learning with multi-classifier showed considering performance in large scale data but much calculation was involved. In this paper, we proposed an incremental multi-classifier (SVM classifiers were used) learning algorithm for large scale imbalanced image annotation. For further accelerating the training and predicting process, Grid?5000, French National Grid, was adopted. The result show that the best performance was reached with only 15-30% of the corpus annotated and our new method could achieve almost the same precision while save nearly 50-60% or even more than 94% of the calculation time when parallel multi-threads were used. Our proposed method will be much potential on very large scale data for less processing time.
Type de document :
Communication dans un congrès
ACM Workshop on Very-Large-Scale Multimedia Corpus, Mining and Retrieval, 2010, Firenze, Italy. ACM, pp.1--6, 2010
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https://hal.inria.fr/hal-00961324
Contributeur : Marie-Christine Fauvet <>
Soumis le : mercredi 19 mars 2014 - 18:30:43
Dernière modification le : mardi 24 avril 2018 - 13:35:13

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  • HAL Id : hal-00961324, version 1

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Bahjat Safadi, Yubing Tong, Georges Quénot. Incremental Multi-Classifier Learning Algorithm on Grid'5000 for Large Scale Image Annotation. ACM Workshop on Very-Large-Scale Multimedia Corpus, Mining and Retrieval, 2010, Firenze, Italy. ACM, pp.1--6, 2010. 〈hal-00961324〉

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