Bayesian diagnosis in expert systems
Artificial Intelligence
Learning and Updating of Uncertainty in Dirichlet Models
Machine Learning
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Color texture segmentation using feature distributions
Pattern Recognition Letters
ImageRover: A Content-Based Image Browser for the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Direct simulation for discrete mixture distributions
Statistics and Computing
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Detection of Region-Duplication Forgery in Digital Image
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram feature-based Fisher linear discriminant for face detection
Neural Computing and Applications
Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 02
An improved Akaike information criterion for state-space model selection
Computational Statistics & Data Analysis
Exposing digital forgeries from JPEG ghosts
IEEE Transactions on Information Forensics and Security
Scale-invariant shape features for recognition of object categories
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Positive vectors clustering using inverted Dirichlet finite mixture models
Expert Systems with Applications: An International Journal
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
An infinite mixture of inverted dirichlet distributions
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Quantifying and Transferring Contextual Information in Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Digital Image Forensics via Intrinsic Fingerprints
IEEE Transactions on Information Forensics and Security
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
IEEE Transactions on Information Forensics and Security - Part 2
Hi-index | 12.05 |
The advent of mixture models has opened the possibility of flexible models which are practical to work with. A common assumption is that practitioners typically expect that data are generated from a Gaussian mixture. The inverted Dirichlet mixture has been shown to be a better alternative to the Gaussian mixture and to be of significant value in a variety of applications involving positive data. The inverted Dirichlet is, however, usually undesirable, since it forces an assumption of positive correlation. Our focus here is to develop a Bayesian alternative to both the Gaussian and the inverted Dirichlet mixtures when dealing with positive data. The alternative that we propose is based on the generalized inverted Dirichlet distribution which offers high flexibility and ease of use, as we show in this paper. Moreover, it has a more general covariance structure than the inverted Dirichlet. The proposed mixture model is subjected to a fully Bayesian analysis based on Markov Chain Monte Carlo (MCMC) simulation methods namely Gibbs sampling and Metropolis-Hastings used to compute the posterior distribution of the parameters, and on Bayesian information criterion (BIC) used for model selection. The adoption of this purely Bayesian learning choice is motivated by the fact that Bayesian inference allows to deal with uncertainty in a unified and consistent manner. We evaluate our approach on the basis of two challenging applications concerning object classification and forgery detection.