Representation of local geometry in the visual system
Biological Cybernetics
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Performance of phase-based algorithms for disparity estimation
Machine Vision and Applications - Special issue on performance evaluation
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
A computational model for visual selection
Neural Computation
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
On Combining One-Class Classifiers for Image Database Retrieval
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Texture-Based Image Retrieval without Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Cluster-Based Statistical Model for Object Detection
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Face Detection Based on Generic Local Descriptors and Spatial Constraints
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A comparison of active classification methods for content-based image retrieval
Proceedings of the 1st international workshop on Computer vision meets databases
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Multilevel Image Coding with Hyperfeatures
International Journal of Computer Vision
Computer Vision and Image Understanding
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
Weakly supervised classification of objects in images using soft random forests
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Dictionary learning in texture classification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Hyperfeatures – multilevel local coding for visual recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Self organizing natural scene image retrieval
Expert Systems with Applications: An International Journal
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This paper presents a method for weakly supervised learning of visual models. The visual model is based on a two-layer image description: a set of “generic” descriptors and their distribution over neighbourhoods. “Generic” descriptors represent sets of similar rotational invariant feature vectors. Statistical spatial constraints describe the neighborhood structure and make our description more discriminant. The joint probability of the frequencies of “generic” descriptors over a neighbourhood is multi-modal and is represented by a set of “neighbourhood-frequency” clusters. Our image description is rotationally invariant, robust to model deformations and characterizes efficiently “appearance-based” visual structure. The selection of distinctive clusters determines model features (common to the positive and rare in the negative examples). Visual models are retrieved and localized using a probabilistic score. Experimental results for “textured” animals and faces show a very good performance for retrieval as well as localization.