作者: De-hong Wang , Sheng Gao , Qi Tian , Wing-kin Sung
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摘要: In this paper, two discriminative fusion schemes are proposed for automatic image annotation. One is the ensemble-pattern association based and another model-based transformation. The approaches studied evaluated in a unified framework AIA on text representation of content MC MFoM learning. flexible fusing diverse visual features multiple modalities. learning can automatically weight most important classification. We evaluate Corel TRECVID 2003 datasets. experimental results clearly show that give significant improvement term mean F1 as well number detected concepts