Application of kernel-genetic algorithm as nonlinear feature selection in tropical wood species recognition system

Computers and Electronics in Agriculture

Volumn 93, Issue , 2013, Pages 68-77

  Yusof, Rubiyah ^a   Khalid, Marzuki ^a   M Khairuddin, Anis Salwa ^b  

^a MALAYSIA JAPAN INTERNATIONAL INSTITUTE OF TECHNOLOGY   (Malaysia)

^b UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Genetic algorithm; Nonlinear feature selection; Texture identification; Wood recognition system

Indexed keywords

FEATURE EXTRACTION; FORESTRY; GENETIC ALGORITHMS; TROPICS;

CLASSIFICATION ACCURACY; ECONOMIC CHALLENGES; KERNEL DISCRIMINANT ANALYSIS; LINEAR FEATURE; NONLINEAR FEATURES; TIMBER INDUSTRY; TROPICAL WOOD SPECIES; WOOD FEATURES; WOOD RECOGNITION;

WOOD;

ACCURACY ASSESSMENT; DATABASE; DISCRIMINANT ANALYSIS; GENETIC ALGORITHM; NONLINEARITY; SAMPLING; TIMBER INDUSTRY; TROPICAL ENVIRONMENT; WOOD;

ALGORITHMS; FORESTRY; PATTERN RECOGNITION; TEXTURE; TROPICS; WOOD;

EID: 84874773345     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2013.01.007     Document Type: Article

References (34)

1. Baudat G., Anouar F. Generalized discriminant analysis using a kernel approach. Neural Computation 2000, 12:2385-2404.
2. Bremanath, R., Nithiya, B., Saipriya R., 2009. Wood species recognition using GLCM and correlation. In: International Conference on Advances in Recent Technologies in Communication and Computing. pp. 615-619.
3. Camargo A., Smith J.S. Image pattern classification for the identification of disease causing agents in plants. Computers and Electronics in Agriculture 2009, 66:121-125.
4. Chowdhury P.R., Deshmukh B., Goswami A.K., Prasad S.S. Neural network based dunal landform mapping from multispectral images using texture features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2011, 4(1):171-184.
5. Eleyan, A., Demiral, H., 2009. Co-occurrence based statistical approach for face recognition. In: 24th International Symposium on Computer and, Information Sciences. pp. 611-615.
6. Grefenstette, J., Thompson, K., Shannon, W., Steinmeyer, B., 2005. Genetic algorithms for feature selection using mantel correlation scoring. In: Classification and Clustering 37th Symposium on the Interface. St. Louis, MO.
7. Howland P., Park H. Generalizing discriminant analysis using the generalized singular value decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(8):995-1006.
8. Hussein, F., Ward, R., Kharma, N., 2001. Genetic algorithms for feature selection and weighting, a review and study. In: Sixth International Conference on Document Analysis and Recognition. pp. 1240-1244.
9. Jiang Y., Chen X., Guo P., Lu H. An improved random sampling LDA for face recognition. Congress on Image and Signal Processing 2008, 2:685-689.
10. Kanan H., Faez K. GA-based optimal selection of PZMI features for face recognition. Applied Mathematics and Computation 2008, 205:706-715.
11. Kandaswamy U., Adjeroh D.A., Lee M.C. Efficient texture analysis of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing 2005, 43(9):2075-2083.
12. Khalid M., Lew Y.L., Yusof R., Nadaraj M. Design of an intelligent wood species recognition system. International Journal of Simulation System, Science and Technology 2008, 9(3):9-19.
13. Khairuddin U., Yusof R., Khalid M., Cordova F. Optimized feature selection for improved tropical wood species recognition system. ICIC express letters, Part B: applications. An International Journal of Research and Surveys 2011, 2(2):441-446.
14. Lu J., Plataniotis K.N., Venetsanopoulos A.N. Face recognition using kernel direct discriminant analysis algorithms. IEEE Transaction on Neural Network 2003, 14(1):117-126.
15. Lu J., Zhao T. Feature selection based-on genetic algorithm for image annotation. Knowledge-Based Systems 2008, 21(8):887-891.
16. Mehdi J.H., Kyani A. Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: activity of carbonic anhydrase II inhibitors. European Journal of Medicinal Chemistry 2007, 42:649-659.
17. Mu T., Pataky T.C., Findlow A.H., Aung M.S.H., Goulermas J.Y. Automated nonlinear feature generation and classification of foot pressure lesions. IEEE Transactions on Information Technology in Biomedicine 2010, 14(2):418-424.
18. Paige C.C., Saunders M.A. Towards a generalized singular value decomposition. SIAM Journal on Numerical Analysis 1981, 18(3):398-405.
19. Pan S., Kudo M. Segmentation of pores in wood microscopic images based on mathematical morphology with a variable structuring element. Computers and Electronics in Agriculture 2011, 75:250-260.
20. Park C.H., Park H. Nonlinear discriminant analysis using kernel functions and the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications 2005, 27(1):87-102.
21. Qin, X., Yang, Y.H., 2004a. Similarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 1. pp. 326-333.
22. Qin, X., Yang, Y.H., 2005b. Basic gray level aura matrices: theory and its application to texture synthesis. In: 10th IEEE Conference on Computer Vision. vol. 1. pp. 128-135.
23. Qin X., Yang Y.H. Aura 3D textures. IEEE Transactions on Visualization and Computer Graphics 2007, 13(2):379-389.
24. Raymer M., Punch W., Goodman E., Kuhn L., Jain A. Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computations 2000, 4(2):164-171.
25. Raymer, M., Punch, W., Goodman, E., Sanschagrin, P., Kuhn, L., 1997. Simultaneous feature extraction and selection using a masking genetic algorithm. In: Proceedings of the 7th International Conference on Genetic Algorithm.
26. Ruiz L.A., Recio J.A., Fernandez-Sarria, Hermosilla T. A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture 2011, 76:284-296.
27. Sun, Y., Babbs, C., Delp, E.A., 2005. Comparison of feature selection methods for the detection of breast cancers in mammograms: adaptive sequential floating search vs. genetic algorithm. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
28. Tuceryan M., Jain A.K. Texture Analysis. The Handbook of Pattern Recognition and Computer Vision 1998, 207-248. Springer. second ed.
29. Wang, K., Bai, X., 2007. Research on classification of wood surface texture based on markov random field. In: 2nd IEEE Conference on Industrial Electronics and Applications. pp. 664-668.
30. Wang, K., Li, X., Wang, W., Duan, S., 2007. Application of kernel method on face feature extraction. In: IEEE International Conference on Mechatronics and Automation.
31. Yang, Q., Tian, Y., 2011. Gait recognition based on KDA and SVM. In: 6th IEEE Joint International Conference on Information Technology and, Artificial Intelligence. pp. 160-163.
32. Ye J., Janardan R., Park C.H., Par H. An optimization criterion for generalized discriminant analysis on undersampled problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(8):982-994.
33. Yu, P., Xu, D., 2008. Palmprint recognition using generalized discriminant analysis. In: International Conference on Audio, Language and Image Processing.
34. Zheng W., Lai J., Yuen P.C. GA-Fisher: A new LDA-Based Face recognition Algorithm With Selection of Principal Components. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 2005, 35(5).