An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
A computational model for visual selection
Neural Computation
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bayesian models for visual information retrieval
Bayesian models for visual information retrieval
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Distinctiveness, Detectability, and Robustness of Local Image Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Spatiotemporal T-Junctions for Occlusion Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Flexible spatial models for grouping local image features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-scale phase-based local features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Discovering Constrained Substructures in Bayesian Trees Using the E.M. Algorithm
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
The quantitative characterization of the distinctiveness and robustness of local image descriptors
Image and Vision Computing
View-independent behavior analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Object re-detection using SIFT and MPEG-7 color descriptors
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Combining models of pose and dynamics for human motion recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Probabilistic combination of visual cues for object classification
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Representing pairwise spatial and temporal relations for action recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Voting by grouping dependent parts
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Visual pattern discovery for architecture image classification and product image search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Object class detection: A survey
ACM Computing Surveys (CSUR)
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In recent years there has been growing interest in recognition models using local image features for applications ranging from long range motion matching to object class recognition systems. Currently, many state-of-the-art approaches have models involving very restrictive priors in terms of the number of local features and their spatial relations. The adoption of such priors in those models are necessary for simplifying both the learning and inference tasks. Also, most of the state-of-the-art learning approaches are semi-supervised batch processes, which considerably reduce their suitability in dynamic environments, where unannotated new images are continuously presented to the learning system. In this work we propose: 1) a new model representation that has a less restrictive prior on the geometry and number of local features, where the geometry of each local feature is influenced by its k closest neighbors and models may contain hundreds of features; and 2) a novel unsupervised on-line learning algorithm that is capable of estimating the model parameters efficiently and accurately. We implement a visual class recognition system using the new model and learning method proposed here, and demonstrate that our system produces competitive classification and localization results compared to state-of-the-art methods. Moreover, we show that the learning algorithm is able to model not only classes with consistent texture (e.g., faces), but also classes with shape only (e.g., leaves), classes with a common shape but with a great variability in terms of internal texture (e.g., cups), and classes of flexible objects (e.g., snake).