Which tags are related to visual content?

Authors:
Yinghai Zhao;Zheng-Jun Zha;Shanshan Li;Xiuqing Wu
Affiliations:
University of Science and Technology of China, Hefei, Anhui, China;National University of Singapore, Singapore;University of Science and Technology of China, Hefei, Anhui, China;University of Science and Technology of China, Hefei, Anhui, China
Venue:
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Year:
2010

Citing 8
Cited 1

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

International Journal of Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Why we tag: motivations for annotation in mobile and online media

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Flickr tag recommendation based on collective knowledge

Proceedings of the 17th international conference on World Wide Web
Learning tag relevance by neighbor voting for social image retrieval

MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Tag ranking

Proceedings of the 18th international conference on World wide web
Learning to tag

Proceedings of the 18th international conference on World wide web

Optimizing social image search with multiple criteria: Relevance, diversity, and typicality

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Photo sharing services allow user to share one's photos on the Web, as well as to annotate the photos with tags. Such web sites currently cumulate large volume of images and abundant tags. These resources have brought forth a lot of new research topics. In this paper, we propose to automatically identify which tags are related to the content of images, i.e. which tags are content-related. A data-driven method is developed to investigate the relatedness between a tag and the image visual content. We conduct extensive experiments over a dataset of 149,915 Flickr images. The experimental results demonstrate the effectiveness of our method.