Incremental Multiple Classifier Active Learning for Concept Indexing in Images and Videos

Bahjat Safadi (1) , Yubing Tong (1) , Georges Quénot (1)

1 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]

Active learning with multiple classifiers has shown good performance for concept indexing in images or video shots in the case of highly imbalanced data. It involves however a large number of computations. In this paper, we propose a new incremental active learning algorithm based on multiple SVM for image and video annotation. The experimental result show that the best performance (MAP) is reached when 15-30% of the corpus is annotated and the new method can achieve almost the same precision while saving 50 to 63% of the computation time.

Mots clés: Active Learning and Incremental Learning; Machine Learning; Multimedia Indexing

Domaines: Recherche d'information [cs.IR]

Bahjat Safadi, Yubing Tong, Georges Quénot. Incremental Multiple Classifier Active Learning for Concept Indexing in Images and Videos. MMM 2011 - International MultiMedia Modeling Conference, Jan 2011, Taipei, Taiwan. pp.240-250, ⟨10.1007/978-3-642-17832-0_23⟩. ⟨hal-00763411⟩