The statistical analysis of compositional data
The statistical analysis of compositional data
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Optimizing aggregate array computations in loops
ACM Transactions on Programming Languages and Systems (TOPLAS)
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICML '06 Proceedings of the 23rd international conference on Machine learning
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
IEEE Transactions on Knowledge and Data Engineering
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We consider the problem of automatic image tagging for online services and explore a prototype-based approach that applies ideas from manifold ranking. Since algorithms for ranking on graphs or manifolds often lack a way of dealing with out of sample data, they are of limited use for pattern recognition. In this paper, we therefore propose to consider diffusion processes over bipartite graphs which allow for a dual treatment of objects and features. As with Google's PageRank, this leads to Markov processes over the prototypes. In contrast to related methods, our model provides a Bayesian interpretation of the transition matrix and enables the ranking and consequently the classification of unknown entities. By design, the method is tailored to histogram features and we apply it to histogram-based color image analysis. Experiments with images downloaded from flickr.com illustrate object localization in realistic scenes.