Maximum-Likelihood Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Experimental perspectives on learning from imbalanced data
Proceedings of the 24th international conference on Machine learning
On population-based simulation for static inference
Statistics and Computing
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Stereo Matching Using Population-Based MCMC
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Context models and out-of-context objects
Pattern Recognition Letters
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Contextual modeling is a critical issue in scene understanding. Object detection accuracy can be improved by exploiting tendencies that are common among object configurations. However, conventional contextual models only exploit the tendencies of normal objects; abnormal objects that do not follow the same tendencies are hard to detect through contextual model. This paper proposes a novel generative model that detects abnormal objects by meeting four proposed criteria of success. This model generates normal as well as abnormal objects, each following their respective tendencies. Moreover, this generation is controlled by a latent scene variable. All latent variables of the proposed model are predicted through optimization via population-based Markov Chain Monte Carlo, which has a relatively short convergence time. We present a new abnormal dataset classified into three categories to thoroughly measure the accuracy of the proposed model for each category; the results demonstrate the superiority of our proposed approach over existing methods.