Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Variational Extensions to EM and Multinomial PCA
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
Epitomic analysis of appearance and shape
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Probabilistic index maps for modeling natural signals
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Computer Vision and Image Understanding
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning natural scene categories by selective multi-scale feature extraction
Image and Vision Computing
ClassCut for unsupervised class segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Object recognition with hierarchical stel models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
2LDA: Segmentation for Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
SURF: speeded up robust features
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
Image analysis by counting on a grid
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Models that captures the common structure of an object class have appeared few years ago in the literature (Jojic and Caspi in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 212---219, 2004; Winn and Jojic in Proceedings of International Conference on Computer Vision (ICCV), pp. 756---763, 2005); they are often referred as "stel models." Their main characteristic is to segment objects in clear, often semantic, parts as a consequence of the modeling constraint which forces the regions belonging to a single segment to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of many meaningful image parts, even when across different images of similar objects, the corresponding parts may not have similar local measurements. Moreover, the segmentation itself is expected to be consistent within a class, although still flexible. These models have been applied mostly to segmentation scenarios.In this paper, we extent those ideas (1) proposing to capture correlations that exist in structural elements of an image class due to global effects, (2) exploiting the segmentations to capture feature co-occurrences and (3) allowing the use of multiple, eventually sparse, observation of different nature. In this way we obtain richer models more suitable to recognition tasks.We accomplish these requirements using a novel approach we dubbed stel component analysis. Experimental results show the flexibility of the model as it can deal successfully with image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features.