Meta-classifiers for multimodal document classification

Autor:	Scott Deeann Chen, Vishal Monga, Pierre Moulin
Rok vydání:	2009
Předmět:	Modalities Computer science business.industry Document classification Feature extraction Image content computer.software_genre Machine learning Visualization Support vector machine ComputingMethodologies_PATTERNRECOGNITION AdaBoost Artificial intelligence business computer Image based
Zdroj:	MMSP
DOI:	10.1109/mmsp.2009.5293343
Popis:	This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bac75e16705924e7b52dc1787ce52cc2 https://doi.org/10.1109/mmsp.2009.5293343 Zobrazit plný text záznamu