International Journal of Computer Vision
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Classification of binary vectors by stochastic complexity
Journal of Multivariate Analysis
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Deterministic annealing EM algorithm
Neural Networks
Spatial Color Indexing and Applications
International Journal of Computer Vision
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
On predictive distributions and Bayesian networks
Statistics and Computing
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Waldo, or Focus of Attention Using Local Color Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Image Classification: City vs. Landscape
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
MDL estimation for small sample sizes and its application to segmenting binary strings
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
ICML '06 Proceedings of the 23rd international conference on Machine learning
Journal of Visual Communication and Image Representation
Using Intrinsic Object Attributes for Incremental Content Based Image Retrieval with Histograms
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Defining a Set of Features Using Histogram Analysis for Content Based Image Retrieval
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Introduction to the special issue on multimedia database
IEEE Transactions on Multimedia
Spatial color descriptor for image retrieval and video segmentation
IEEE Transactions on Multimedia
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
Expert system design using wavelet and color vocabulary trees for image retrieval
Expert Systems with Applications: An International Journal
Spatial color image segmentation based on finite non-Gaussian mixture models
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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-Bernoulli mixtures. For the estimation of the model's parameters, we use a maximum a posteriori (MAP) approach through deterministic annealing expectation maximization (DAEM). Smoothing priors on the components parameters are introduced to stabilize the estimation. The selection of the number of clusters is based on stochastic complexity. The results show that our model achieves good performance in some image classification problems.