Learning Class Specific Graph Prototypes
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
From region based image representation to object discovery and recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
From a set of shapes to object discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning shape detector by quantizing curve segments with multiple distance metrics
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Representing and recognizing objects with massive local image patches
Pattern Recognition
Unsupervised feature selection and category formation for generic object recognition
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Interactive image segmentation by matching attributed relational graphs
Pattern Recognition
A probabilistic model for component-based shape synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Learning a generative model of images by factoring appearance and shape
Neural Computation
Bottom-up perceptual organization of images into object part hypotheses
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Unsupervised discovery of mid-level discriminative patches
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Human activities as stochastic kronecker graphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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Suppose a set of arbitrary (unlabeled) images contains frequent occurrences of 2D objects from an unknown category. This paper is aimed at simultaneously solving the following related problems: (1) unsupervised identification of photometric, geometric, and topological properties of multiscale regions comprising instances of the 2D category; (2) learning a region-based structural model of the category in terms of these properties; and (3) detection, recognition and segmentation of objects from the category in new images. To this end, each image is represented by a tree that captures a multiscale image segmentation. The trees are matched to extract the maximally matching subtrees across the set, which are taken as instances of the target category. The extracted subtrees are then fused into a tree-union that represents the canonical category model. Detection, recognition, and segmentation of objects from the learned category are achieved simultaneously by finding matches of the category model with the segmentation tree of a new image. Experimental validation on benchmark datasets demonstrates the robustness and high accuracy of the learned category models, when only a few training examples are used for learning without any human supervision.