Skip to Main content Skip to Navigation
Conference papers

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

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.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-00961324
Contributor : Marie-Christine Fauvet <>
Submitted on : Wednesday, March 19, 2014 - 6:30:43 PM
Last modification on : Tuesday, December 8, 2020 - 10:42:46 AM

Identifiers

  • HAL Id : hal-00961324, version 1

Collections

Citation

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. pp.1--6. ⟨hal-00961324⟩

Share

Metrics

Record views

278