Genetic programming for multiclass texture classification using a small number of instances

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8886, Issue , 2014, Pages 335-346

  Al Sahaf, Harith ^a   Zhang, Mengjie ^a   Johnston, Mark ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Genetic Programming; Multiclass; Texture Classification

Indexed keywords

GENETIC ALGORITHMS; GENETIC PROGRAMMING; IMAGE CLASSIFICATION; PIXELS; TEXTURES; PATTERN RECOGNITION;

DOMAIN SPECIFIC; HUMAN INTERVENTION; MULTI-CLASS CLASSIFICATION; MULTICLASS; PIXEL VALUES; TEXTURE CLASSIFICATION; TEXTURE DATA SET; TEXTURE IMAGE;

CLASSIFICATION (OF INFORMATION);

EID: 84921341804     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-13563-2_29     Document Type: Article

References (18)

1. Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications (1999)
2. Fix, E., Hodges, J.: Discriminatory analysis-nonparametric discrimination: Consistency properties. Technical Report 4, USAF School of Aviation Medicine, Randolf Field, Texas (1951)
3. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations Newsletter 11(1), 10-18 (2009)
4. Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610-621 (1973)
5. Kylberg, G.: The Kylberg texture dataset v. 1.0. External report (Blue series) 35, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Uppsala, Sweden (2011)
6. Levner, I., Bulitko, V., Li, L., Lee, G., Greiner, R.: Automated feature extraction for object recognition. In: Proceedings of the Image and Vision Computing, New Zealand, pp. 309-314. Massey University (2003)
7. Loveard, T., Ciesielski, V.: Representing classification problems in genetic programming. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 1070-1077. IEEE Press (2001)
8. Luke, S.: Essentials of Metaheuristics. Lulu, 2nd edn. (2013)
9. Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3(2), 199-230 (1995)
10. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971-987 (2002)
11. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods, pp. 185-208. MIT Press (1999)
12. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu (2008) (With contributions by J. R. Koza)
13. Song, A., Loveard, T., Ciesielski, V.: Towards genetic programming for texture classification. In: Stumptner, M., Corbett, D.R., Brooks, M. (eds.) Canadian AI 2001. LNCS (LNAI), vol. 2256, pp. 461-472. Springer, Heidelberg (2001)
14. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
15. Zhang, L., Jack, L., Nandi, A.: Extending genetic programming for multi-class classification by combining k-nearest neighbor. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. v/349-v/352 (2005)
16. Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N.Y. (ed.) AI 1999. LNCS, vol. 1747, pp. 180-192. Springer, Heidelberg (1999)
17. Zhang, M., Ciesielski, V.B., Andreae, P.: A domain-independent window approach to multiclass object detection using genetic programming. EURASIP Journal on Advances in Signal Processing 2003(8), 841-859 (2003)
18. Zhang, M., Johnston, M.: A variant program structure in tree-based genetic programming for multiclass object classification. In: Cagnoni, S. (ed.) Evolutionary Image Analysis and Signal Processing. SCI, vol. 213, pp. 55-72. Springer, Heidelberg (2009)