Fusing heterogeneous modalities for video and image re-ranking

Multimedia documents in popular image and video sharing websites such as Flickr and Youtube are heterogeneous documents with diverse ways of representations and rich user-supplied information. In this paper, we investigate how the agreement among heterogeneous modalities can be exploited to guide data fusion. The problem of fusion is cast as the simultaneous mining of agreement from different modalities and adaptation of fusion weights to construct a fused graph from these modalities. An iterative framework based on agreement-fusion optimization is thus proposed. We plug in two well-known algorithms: random walk and semi-supervised learning to this framework to illustrate the idea of how agreement (conflict) is incorporated (compromised) in the case of uniform and adaptive fusion. Experimental results on web video and image re-ranking demonstrate that, by proper fusion strategy rather than simple linear fusion, performance improvement on search can generally be expected.