Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
Image annotation via graph learning
Pattern Recognition
Label to region by bi-layer sparsity priors
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Image annotation using multi-correlation probabilistic matrix factorization
Proceedings of the international conference on Multimedia
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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.