Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Large scale natural image classification by sparsity exploration
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Using large-scale web data to facilitate textual query based retrieval of consumer photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Label to region by bi-layer sparsity priors
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Context-based multi-label image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Automatic image tagging through information propagation in a query log based graph structure
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Image tagging is an important technique for the image content understanding and text based image processing. Given a selection of images, how to tag these images efficiently and effectively is an interesting problem. In this paper, a novel semi-auto image tagging technique is proposed: By assigning each image a category label first, our method can automatically recommend those promising tags to each image by utilizing existing vast web data. The main contributions of our paper can be highlighted as follows: (i) By assigning each image a category label, our method can automatically recommend other tags to the image, thus reducing the human annotation efforts. Meanwhile, our method guarantee tags' diversity due to abundant web data. (ii) We use sparse coding to automatically select those semantically related images for tag propagation. (iii) Local & global ranking agglomeration will make our method robust to noisy tags. We use Event dataset as the images to be tagged, and crawled Flickr images with their associated tags according to the category label in Event dataset as the auxiliary web data. Experimental results show that our method achieves promising performance for image tagging, which proves the effectiveness of our method.