Attributed Graph Mining and Matching:

An Attempt to Define and Extract Soft Attributed Patterns



What are we doing?





Given an initial graph template and a set of attributed relational graphs (ARGs), this method modifies the graph template into the common subgraph pattern among the ARGs with the MAXIMAL graph size, by discovering probably missing nodes, deleting redundant nodes, and training the attributes.



What is the significance?



Spotlight video



Reference

Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, “Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns”, in Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2014. (PDF) (code)


Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns


Extented Application 1: As a platform of category modeling from cluttered scenes, this technique has been applied to train category models for 3D reconstruction from ubiquitous images.
Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, "When 3D Reconstruction Meets Ubiquitous RGB-D Images", in Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2014. (PDF

Extended Application 1: Learn 3D Reconstruction from Ubiquitous RGB-D Images

Spotlight for Application 1


Extented Application 2: Recovering the model for the whole object from a fragment using cluttered (web) images.
Given a set of cluttered scenes (web images) that contain objects in the target category, this technique can be applied to recover the category model from "an object fragment".


Extented Application 3: Mine a deformable model from unlabeled videos for tracking and pose estimation of animals.

Deformable models mined from unlabeled videos


Web image dataset (download) for visual mining.

Please contact Quanshi Zhang (Website), if you have questions.
Email: zhangqs@g.ucla.edu

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