Please use this identifier to cite or link to this item: https://doi.org/10.1109/MMSP.2005.248595
Title: Discriminative fusion approach for automatic image annotation
Authors: Wang, D.-H.
Gao, S.
Tian, Q.
Sung, W.-K. 
Keywords: Discriminative fusion
Image annotation
Maximum figure of merit
Issue Date: 2006
Source: Wang, D.-H.,Gao, S.,Tian, Q.,Sung, W.-K. (2006). Discriminative fusion approach for automatic image annotation. 2005 IEEE 7th Workshop on Multimedia Signal Processing. ScholarBank@NUS Repository. https://doi.org/10.1109/MMSP.2005.248595
Abstract: In this paper, two discriminative fusion schemes are proposed for automatic image annotation. One is the ensemble-pattern association based fusion and another is the model-based transformation. The fusion approaches are studied and evaluated in a unified framework for AIA based on the text representation of the image content and the MC MFoM learning. The schemes are flexible for fusing diverse visual features and multiple modalities. The discriminative learning can automatically weight the most important features for the classification. We evaluate the fusion schemes based on the Corel and TRECVID 2003 datasets. The experimental results clearly show that the proposed fusion schemes give a significant improvement in term of the mean of F1 as well as the number of the detected concepts.
Source Title: 2005 IEEE 7th Workshop on Multimedia Signal Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/40200
ISBN: 0780392892
DOI: 10.1109/MMSP.2005.248595
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