A Statistical Model for Histogram Refinement

Authors:
Nizar Bouguila;Walid Elguebaly
Affiliations:
CIISE, Concordia University, Montreal, Canada;CIISE, Concordia University, Montreal, Canada
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

Citing 13
Cited 0

Color indexing

International Journal of Computer Vision
Comparing images using color coherence vectors

MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Deterministic annealing EM algorithm

Neural Networks
On Image Classification: City vs. Landscape

CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Histogram refinement for content-based image retrieval

WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Formal multiple-bernoulli models for language modeling

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution

ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach

IEEE Transactions on Knowledge and Data Engineering
Unsupervised learning of a finite discrete mixture: Applications to texture modeling and image databases summarization

Journal of Visual Communication and Image Representation
High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length

IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of Count Data Using Generalized Dirichlet Multinomial Distributions

IEEE Transactions on Knowledge and Data Engineering
On discrete data clustering

PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Color histograms have been widely used successfully in many computer vision and image processing applications. However, they do not include any spatial information. In this paper, we propose a statistical model to integrate both color and spatial information. Our model is based on finite multiple-Beta-Bernoulli mixtures. The results show that our model achieves good performance in a specific image classification problem (city vs. landscape).