Selection of Generative Models in Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations and Trends® in Computer Graphics and Vision
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Bayesian space conceptualization and place classification for semantic maps in mobile robotics
Robotics and Autonomous Systems
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Tracking by Hierarchical Representation of Target Structure
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
An Integrated Method for Multiple Object Detection and Localization
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Probabilistic Pose Recovery Using Learned Hierarchical Object Models
Cognitive Vision
A Bag of Strings Representation for Image Categorization
Journal of Mathematical Imaging and Vision
Classification of silhouettes using contour fragments
Computer Vision and Image Understanding
Unsupervised modeling of objects and their hierarchical contextual interactions
Journal on Image and Video Processing - Special issue on patches in vision
Boosting Shift-Invariant Features
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Closed-loop learning of visual control policies
Journal of Artificial Intelligence Research
Embedding spatial information into image content description for scene retrieval
Pattern Recognition
Modeling temporal structure of decomposable motion segments for activity classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Discovering multipart appearance models from captioned images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Voting by grouping dependent parts
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Human posture recognition for intelligent vehicles
Journal of Real-Time Image Processing
Continuous surface-point distributions for 3D object pose estimation and recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Sparse flexible models of local features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A novel object categorization model with implicit local spatial relationship
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A probabilistic model for component-based shape synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Discovering hierarchical object models from captioned images
Computer Vision and Image Understanding
Visual pattern discovery for architecture image classification and product image search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Learning a generative model of images by factoring appearance and shape
Neural Computation
An automated vision based on-line novel percept detection method for a mobile robot
Robotics and Autonomous Systems
Object class detection: A survey
ACM Computing Surveys (CSUR)
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We propose a generative model that codes the geometry and appearance of generic visual object categories as a loose hierarchy of parts, with probabilistic spatial relations linking parts to subparts, soft assignment of subparts to parts, and scale invariant keypoint based local features at the lowest level of the hierarchy. The method is designed to efficiently handle categories containing hundreds of redundant local features, such as those returned by current key-point detectors. This robustness allows it to outperform constellation style models, despite their stronger spatial models. The model is initialized by robust bottom-up voting over location-scale pyramids, and optimized byExpectation-Maximization. Training is rapid, and objects do not need to be marked in the training images. Experiments on several popular datasets show the methodýs ability to capture complex natural object classes.