Multimodal graph-based reranking for web image search

Publisher:
IEEE
Publication Type:
Journal Article
Citation:
IEEE Transactions On Image Processing, 2012, 21 (11), pp. 4649 - 4661
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012002974OK.pdf1.36 MB
Adobe PDF
This paper introduces a web image search reranking approach that explores multiple modalities in a graphbased 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 uni?ed 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.
Please use this identifier to cite or link to this item: