Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A Hierarchical Model for the Recognition of Deformable Objects
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Compositional object recognition, segmentation, and tracking in video
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Scene modelling and classification using learned spatial relations
COSIT'09 Proceedings of the 9th international conference on Spatial information theory
Exploiting low-level image segmentation for object recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Learning compositional categorization models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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This contribution proposes a compositionality architecture for visual object categorization, i.e., learning and recognizing multiple visual object classes in unsegmented, cluttered real-world scenes. We propose a sparse image representation based on localized feature histograms of salient regions. Category specific information is then aggregated by using relations from perceptual organization to form compositions of these descriptors. The underlying concept of image region aggregation to condense semantic information advocates for a statistical representation founded on graphical models. On the basis of this structure, objects and their constituent parts are localized. To complement the learned dependencies between compositions and categories, a global shape model of all compositions that form an object is trained. During inference, belief propagation reconciles bottom-up feature-driven categorization with top-down category models. The system achieves a competitive recognition performance on the standard CalTech database.