Genetic Algorithms for Feature Selection and Weighting, A Review and Study
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
SIAM Journal on Matrix Analysis and Applications
Basic Gray Level Aura Matrices: Theory and its Application to Texture Synthesis
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
IEEE Transactions on Visualization and Computer Graphics
An Improved Random Sampling LDA for Face Recognition
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Feature selection based-on genetic algorithm for image annotation
Knowledge-Based Systems
Image pattern classification for the identification of disease causing agents in plants
Computers and Electronics in Agriculture
Wood Species Recognition Using GLCM and Correlation
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Automated nonlinear feature generation and classification of foot pressure lesions
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Computers and Electronics in Agriculture
Original papers: A feature extraction software tool for agricultural object-based image analysis
Computers and Electronics in Agriculture
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Classifying tropical wood species pose a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. Previous works on tropical wood species recognition systems considered methods for classification of linear features of the wood species. However, tropical wood species are known to exhibit nonlinear features due to several factors such as age of the tree, samples taken from different parts of the tree, etc. to address the nonlinear features of the tropical wood species, a Kernel-Genetic Algorithm (K-GA) technique for feature selection is proposed. This method combines the Kernel Discriminant Analysis (KDA) technique with Genetic Algorithm (GA) to generate nonlinear wood features and at the same time reduce dimension of the wood database. The proposed system achieved a classification accuracy of 98.69%, showing marked improvement to the work done previously.