What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Discrete Applied Mathematics
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Recommending Tags for Pictures Based on Text, Visual Content and User Context
ICIW '08 Proceedings of the 2008 Third International Conference on Internet and Web Applications and Services
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Quest for relevant tags using local interaction networks and visual content
Proceedings of the international conference on Multimedia information retrieval
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Large-scale image retrieval benchmarks invariably consist of images from the Web. Many of these benchmarks are derived from online photo sharing networks, like Flickr, which in addition to hosting images also provide a highly interactive social community. Such communities generate rich metadata that can naturally be harnessed for image classification and retrieval. Here we study four popular benchmark datasets, extending them with social-network metadata, such as the groups to which each image belongs, the comment thread associated with the image, who uploaded it, their location, and their network of friends. Since these types of data are inherently relational, we propose a model that explicitly accounts for the interdependencies between images sharing common properties. We model the task as a binary labeling problem on a network, and use structured learning techniques to learn model parameters. We find that social-network metadata are useful in a variety of classification tasks, in many cases outperforming methods based on image content.