Affine invariant topic model for generic object recognition

Authors:
Zhenxiao Li;Liqing Zhang
Affiliations:
MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University;MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

Citing 7
Cited 0

Unsupervised learning by probabilistic latent semantic analysis

Machine Learning
Latent dirichlet allocation

The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
A Bayesian Hierarchical Model for Learning Natural Scene Categories

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel topic model named Affine Invariant Topic Model(AITM) for generic object recognition Abandoning the “bag of words” assumption in traditional topic models, AITM incorporates spatial structure into traditional LDA AITM extends LDA by modeling visual words with latent affine transformations as well as latent topics, treating topics as different parts of objects and assuming a common affine transformation of visual words given a certain topic MCMC is employed to make inference for latent variables, MCMC-EM algorithm is used to parameter estimation, and Bayesian decision rule is used to perform classification Experiments on two challenging data sets demonstrate the efficiency of AITM.