IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
A color image segmentation approach for content-based image retrieval
Pattern Recognition
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
International Journal of Remote Sensing
Integrating spatial and color information in images using a statistical framework
Expert Systems with Applications: An International Journal
Online clustering via finite mixtures of Dirichlet and minimum message length
Engineering Applications of Artificial Intelligence
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
On fitting finite dirichlet mixture using ECM and MML
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Exploiting low-level image segmentation for object recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
MML-Based approach for finite dirichlet mixture estimation and selection
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and Classification
IEEE Transactions on Knowledge and Data Engineering
Journal of Visual Communication and Image Representation
Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Finite mixture models are one of the most widely and commonly used probabilistic techniques for image segmentation. Although the most well known and commonly used distribution when considering mixture models is the Gaussian, it is certainly not the best approximation for image segmentation and other related image processing problems. In this paper, we propose and investigate the use of several other mixture models based namely on Dirichlet, generalized Dirichlet and Beta-Liouville distributions, which offer more flexibility in data modeling, for image segmentation. A maximum likelihood (ML) based algorithm is applied for estimating the resulted segmentation model's parameters. Spatial information is also employed for figuring out the number of regions in an image and several color spaces are investigated and compared. The experimental results show that the proposed segmentation framework yields good overall performance, on various color scenes, that is better than comparable techniques.