Machine Learning
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Bayesian parameter estimation via variational methods
Statistics and Computing
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Journal of Machine Learning Research
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Kernel conditional random fields: representation and clique selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Semantic-Shift for Unsupervised Object Detection
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
International Journal of Computer Vision
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
A spatially aware generative model for image classification, topic discovery and segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Large margin cost-sensitive learning of conditional random fields
Pattern Recognition
Image Segmentation with a Unified Graphical Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation with topic random field
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Hidden semantic concept discovery in region based image retrieval
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
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Latent topic models have become a popular paradigm in many computer vision applications due to their capability to unsupervisely discover semantics in visual content. Relying on the Bag-of-Words representation, they consider images as mixtures of latent topics that generate visual words according to some specific distributions. However, the performance of these methods is still limited by the way in which they take into account the spatial distribution of visual words and, what is even more important, the currently used appearance distributions. In this paper, we propose a novel region-centered latent topic model that introduces two main contributions: first, an improved spatial context model that allows for considering inter-topic inter-region influences; and second, an advanced region-based appearance distribution built on the Kernel Logistic Regressor. It is worth highlighting that the proposed contributions have been seamlessly integrated in the model, so that all the parameters are concurrently estimated using a unified inference process. Furthermore, the proposed model has been extended to work in both unsupervised and supervised modes. Our results for unsupervised mode improve 30% those of previous latent topic models. For supervised mode, where discriminative approaches are preponderant, our results are quite close to those of discriminative state-of-the-art methods.