Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Behavior histograms for action recognition and human detection
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Discriminative sparse representations for cervigram image segmentation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Histograms of visual words (or textons) have proved effective in tasks such as image classification and object class recognition. A common approach is to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classification is then achieved by k-nearest neighbour search over the exemplars. In this paper we introduce two novelties on this approach: (i) we show that new compact single histogram models estimated optimally from the entire training set achieve an equal or superior classification accuracy. The benefit of the single histograms is that they are much more efficient both in terms of memory and computational resources; and (ii) we show that bag of visual words histograms can provide an accurate pixel-wise segmentation of an image into object class regions. In this manner the compact models of visual object classes give simultaneous segmentation and recognition of image regions. The approach is evaluated on the MSRC database [5] and it is shown that performance equals or is superior to previous publications on this database.