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
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Scalable search-based image annotation of personal images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
EnjoyPhoto: a vertical image search engine for enjoying high-quality photos
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
A review on automatic image annotation techniques
Pattern Recognition
Rotation Invariant Curvelet Features for Region Based Image Retrieval
International Journal of Computer Vision
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
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In this paper, we propose a novel Markov model-based formulation for the image annotation problem. In this formulation, we treat image annotation as a graph ranking problem, by defining all possible labels in the lexicon as the states of a Markov chain. To fully utilize the correlation between labels, a query-biased transition matrix is dynamically constructed according to the query image. Based on this formulation, a scalable Markov model-based image annotation (MBIA) algorithm is presented to rank all the possible labels. To be scalable, we adapt search techniques on a Web-scale image set, and make MBIA capable of annotating arbitrary images with unlimited vocabulary. By fully exploring the correlation between labels, MBIA leads to superior performance than standard techniques. Experimental results on the typical Corel dataset and U. Washington dataset show the effectiveness and efficiency of the proposed algorithm.