Social tag alignment with image regions by sparse reconstructions.
Proceedings of the 20th ACM international conference on Multimedia(2012)
摘要
How to align social tags with image regions without additional human intervention is a challenging but a valuable task since it can provide more detailed image semantic information and improve the accuracy of image retrieval. To this end, we propose a novel tag-to-region method with two phases of sparse reconstructions by exploring the large-scale user contributed resources. Given an image with social tags, we first explore the tagging information of large-scale social images to sparsely reconstruct the label vector of the given image, and then use the reconstructing weights as the semantic relevance to the image. With the top $T$ semantically relevant images, we further employ a group sparse coding algorithm to reconstruct each region of the given image, in which the regions from the social images with a common label are deemed as a label group. The group sparsity works on the assumption that one image region corresponds to tags as few as possible. Finally, the region-level tags can be predicted based on the reconstruction error in the corresponding label groups. Extensive experiments on MSRC and SAIAPR TC-12 datasets demonstrate the encouraging performance of our method in comparison with other baselines.
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