Multimodal graph-based reranking for web image search.

This paper introduces a web image search reranking approach that explores multiple modalities in a graph-based learning scheme. Different from the conventional methods that usually adopt a single modality or integrate multiple modalities into a long feature vector, our approach can effectively integrate the learning of relevance scores, weights of modalities, and the distance metric and its scaling for each modality into a unified scheme. In this way, the effects of different modalities can be adaptively modulated and better reranking performance can be achieved. We conduct experiments on a large dataset that contains more than 1000 queries and 1 million images to evaluate our approach. Experimental results demonstrate that the proposed reranking approach is more robust than using each individual modality, and it also performs better than many existing methods.