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SIAM Journal on Scientific and Statistical Computing
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
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Flexible Content-based Image Retrieval System with Combined Scene Description Keyword
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
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
The Journal of Machine Learning Research
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fast Approximate Similarity Search in Extremely High-Dimensional Data Sets
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Evaluating the impact of selection noise in community-based web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Graph Based Approach for Naming Faces in News Photos
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An adaptive graph model for automatic image annotation
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image annotation by large-scale content-based image retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Enhancing image annotation by integrating concept ontology and text-based bayesian learning model
Proceedings of the 15th international conference on Multimedia
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Pagerank based clustering of hypertext document collections
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
VisualRank: Applying PageRank to Large-Scale Image Search
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
Using Semantics for Speech Annotation of Images
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
A Novel Graph-based Image Annotation with Two Level Bag Generators
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
RankCompete: simultaneous ranking and clustering of web photos
Proceedings of the 19th international conference on World wide web
Why do people tag?: motivations for photo tagging
Communications of the ACM
Technique of Image Retrieval Based on Multi-label Image Annotation
MMIT '10 Proceedings of the 2010 Second International Conference on MultiMedia and Information Technology - Volume 02
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
Laplacian Support Vector Machines Trained in the Primal
The Journal of Machine Learning Research
Knowledge propagation in large image databases using neighborhood information
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A correlation approach for automatic image annotation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Face Annotation Using Transductive Kernel Fisher Discriminant
IEEE Transactions on Multimedia
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
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The practicality of large-scale image indexing and querying methods depends crucially upon the availability of semantic information. The manual tagging of images with semantic information is in general very labor intensive, and existing methods for automated image annotation may not always yield accurate results. The aim of this paper is to reduce to a minimum the amount of human intervention required in the semantic annotation of images, while preserving a high degree of accuracy. Ideally, only one copy of each object of interest would be labeled manually, and the labels would then be propagated automatically to all other occurrences of the objects in the database. To this end, we propose an influence propagation strategy, SW-KProp, that requires no human intervention beyond the initial labeling of a subset of the images. SW-KProp distributes semantic information within a similarity graph defined on all images in the database: each image iteratively transmits its current label information to its neighbors, and then readjusts its own label according to the combined influences of its neighbors. SW-KProp influence propagation can be efficiently performed by means of matrix computations, provided that pairwise similarities of images are available. We also propose a variant of SW-KProp which enhances the quality of the similarity graph by selecting a reduced feature set for each prelabeled image and rebuilding its neighborhood. The performances of the SW-KProp method and its variant were evaluated against several competing methods on classification tasks for three image datasets: a handwritten digit dataset, a face dataset and a web image dataset. For the digit images, SW-KProp and its variant performed consistently better than the other methods tested. For the face and web images, SW-KProp outperformed its competitors for the case when the number of prelabeled images was relatively small. The performance was seen to improve significantly when the feature selection strategy was applied.