Please use this identifier to cite or link to this item:
|Title:||Discriminative fusion approach for automatic image annotation|
Maximum figure of merit
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 11, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.