Boosting of Maximal Figure of Merit Classifiers for Automatic Image Annotation
作者： Filippo Vella , Chin-Hui Lee , Salvatore Gaglio
DOI: 10.1109/ICIP.2007.4379131
关键词: Pattern recognition 、 Figure of merit 、 Random subspace method 、 Computer science 、 Boosting methods for object categorization 、 Discriminative model 、 Boosting (machine learning) 、 Artificial intelligence 、 Text mining 、 Automatic image annotation 、 Machine learning
摘要: Visual information contained in a scene is very complex and can be represented with multiple features describing aspects of the entire information. In this paper we propose boosting approach to automatic image annotation by building strong classifiers based on collections weak concept each collection focused single visual feature. The are trained maximal figure-of-merit learning approach. By exploiting procedure allows build able pick most discriminative feature for specific task.
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