FotoFile: a consumer multimedia organization and retrieval system
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Determining number of clusters and prototype locations via multi-scale clustering
Pattern Recognition Letters
MiAlbum - a system for home photo managemet using the semi-automatic image annotation approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automated annotation of human faces in family albums
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Leveraging face recognition technology to find and organize photos
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
ACM SIGGRAPH 2006 Papers
Extreme video retrieval: joint maximization of human and computer performance
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Tag refinement by regularized LDA
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Smart batch tagging of photo albums
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Active tagging for image indexing
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
One person labels one million images
Proceedings of the international conference on Multimedia
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Image Decomposition With Multilabel Context: Algorithms and Applications
IEEE Transactions on Image Processing
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Interactive tagging is an approach that combines human and computer to assign descriptive keywords to image contents in a semi-automatic way. It can avoid the problems in automatic tagging and pure manual tagging by achieving a compromise between tagging performance and manual cost. However, conventional research efforts on interactive tagging mainly focus on sample selection and models for tag prediction. In this work, we investigate interactive tagging from a different aspect. We introduce an interactive image tagging framework that can more fully make use of human's labeling efforts. That means, it can achieve a specified tagging performance by taking less manual labeling effort or achieve better tagging performance with a specified labeling cost. In the framework, hashing is used to enable a quick clustering of image regions and a dynamic multiscale clustering labeling strategy is proposed such that users can label a large group of similar regions each time. We also employ a tag refinement method such that several inappropriate tags can be automatically corrected. Experiments on a large dataset demonstrate the effectiveness of our approach