Incrementally discovering object classes using similarity propagation and graph clustering

Authors:
Shengping Xia;Edwin R. Hancock
Affiliations:
ATR Lab, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P.R.China;Department of Computer Science, University of York, York, UK
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Year:
2009

Citing 8
Cited 0

Unsupervised learning by probabilistic latent semantic analysis

Machine Learning
Video Google: A Text Retrieval Approach to Object Matching in Videos

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints

International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
3D Object Recognition Using Hyper-Graphs and Ranked Local Invariant Features

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A robust approach for object recognition

PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We are interested in incrementally discovering the set of object classes present in a scalable database of images This paper describes a graph-based framework for learning the set of object classes in a weakly supervisedly manner Rather than making use of the ”Bag-of-Features (BoF)” approach widely used in current work on object recognition, we represent each image by a graph using a group of selected local invariant features Using local feature matching and iterative Procrustes alignment, we perform graph matching and compute a similarity measure Borrowing the idea of query expansion, we develop a similarity propagation based graph clustering (SPGC) method Using this method class specific clusters of the graphs can be obtained Such a cluster can be generally represented by using a higher level graph model whose vertices are the clustered graphs, and the edge weights are determined by the pairwise similarity measure Experiments are performed on a dataset, in which the number of images increases from 1 to 50K and the number of objects increases from 1 to over 500 Some objects have been discovered with total recall and a precision 1 in a single cluster.