Fundamentals of digital image processing
Fundamentals of digital image processing
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Automatic Detection of Human Nudes
International Journal of Computer Vision - 1998 Marr Prize
Content-Based Image Retrieval at the End of the Early Years
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
On representation and matching of multi-coloured objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Image compression using binary space partitioning trees
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion
IEEE Transactions on Circuits and Systems for Video Technology
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In this article, a novel Local Experts Organization (LEO) model for processing tree structures with its application of natural scene images classification is presented. Instead of relatively poor representation of image features in a flat vector form, we proposed to extract the features and encode them into a binary tree representation. The proposed LEO model is used to generalize this tree representation in order to perform the classification task. The capabilities of the proposed LEO model are evaluated in simulations running under different image scenarios. Experimental results demonstrate that the LEO model is consistent in terms of robustness amongst the other tested classifiers.