Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Manifold-ranking based video concept detection on large database and feature pool
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Online multi-label active annotation: towards large-scale content-based video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Knowledge discovery over community-sharing media: from signal to intelligence
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Narrative image composition using objective and subjective tagging
ACM SIGGRAPH 2010 Posters
Proceedings of the international conference on Multimedia
Tagging image by exploring weighted correlation between visual features and tags
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Improving image tags by exploiting web search results
Multimedia Tools and Applications
Towards optimizing human labeling for interactive image tagging
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Hi-index | 0.00 |
Concept labeling and ontology-free tagging are the two typical manners of image annotation. Despite extensive research efforts have been dedicated to labeling, currently automatic image labeling algorithms are still far from satisfactory, and meanwhile manual labeling is rather labor-intensive. In contrast with labeling, tagging works in a free way and therefore it has better user experience for annotators. In this paper, we introduce an active tagging scheme that combines human and computer to assign tags to images. The scheme works in an iterative way. In each round, the most informative images are selected for manual tagging, and the remained images can be annotated by a tag prediction component. We have integrated multiple criteria for sample selection, including ambiguity, citation, and diversity. Experiments are conducted on different datasets and empirical results have demonstrated the effectiveness of the proposed approach.