Experimentos computacionales con métodos lineales en el reconocimiento taxonómico de insectos
Keywords:
taxonomy, spectral decomposition, recognition, morphometrics, PCA, LDA, LPP, SRDAAbstract
Object recognition methods are applied for taxonomic classification by extracting characteristic information of insect wing images. We analysed wing images of two insect groups (Hemiptera: Triatominae and Ceratopogonidae: Culicoides) and different taxonomic levels (genus, subgenus and species). Instead of using a traditional method such as geometric morphometry, which requires the prior digitization of coordinates that explain the wing geometry, we processed the complete noisy images using lineal methods. Our results show that methods based on supervised training achieve, on average, the same outcome as the traditional method, which indeed suggests that the entire wing structure has relevant taxonomic information.
Downloads
Metrics
References
Bartlett, M. S. (2001). Independent Component Representations for Face Recognition BT - Face Image Analysis by Unsupervised Learning. In M. S. Bartlett (Ed.) (pp. 39-67). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4615-1637-8_3
Belhumeur, P., Hespanha, J., Kriegman, D. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720. https://doi.org/10.1109/34.598228
Bledsoe, W. W. (1966). The model method in facial recognition. Panoramic Research Inc., Palo Alto, CA, Rep. PR1, 15, 47.
Bruce, V. (1988). Recognising faces. Essays in cognitive psychology. Hillsdale, NJ: Lawrence Erlbaum Associates.
Cai, D., He, X., Han, J. (2007). Spectral Regression: A Unified Approach for Sparse Subspace Learning. In Seventh IEEE International Conference on Data Mining (ICDM 2007) (pp. 73-82). https://doi.org/10.1109/ICDM.2007.89
Cai, D., He, X., Han, J. (2008). SRDA: An efficient algorithm for large-scale discriminant analysis. IEEE Transactions on Knowledge and Data Engineering, 20(1), 1-12.
Chellappa, R., Wilson, C. L., Sirohey, S. (1995). Human and machine recognition of faces: a survey. Proceedings of the IEEE, 83(5), 705–741. https://doi.org/10.1109/5.381842
Chung, F. R. K. (1997). Spectral graph theory. American Mathematical Soc.
Coomans, D., Massart, D. L. (1982). Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-Nearest neighbour classification by using alternative voting rules. Analytica Chimica Acta, 136, 15-27.
de Carvalho, M. R., Bockmann, F. A., Amorim, D. S., Brandão, C. R. F. (2008). Systematics must Embrace Comparative Biology and Evolution, not Speed and Automation. Evolutionary Biology, 35(2), 150–157. https://doi.org/10.1007/s11692-008-9018-7
Favret, C., Sieracki, J. (2015). Machine vision automated species identification scaled towards production levels. Systematic Entomology, 41(1), 133–143. https://doi.org/10.1111/syen.12146
He, X., Niyogi, P. (2004). Locality Preserving Projections. In: Thrun, S., Saul,L. K., Schölkopf, B. (Eds.), Advances in Neural Information Processing Systems 16 (pp. 153–160). MIT Press. Retrieved from http://papers.nips.cc/paper/2359-locality-preserving-projections.pdf
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.-J. (2005). Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3), 328-340. https://doi.org/10.1109/TPAMI.2005.55
Linsker, R. (1988). Self-organization in a perceptual network. Computer, 21(3), 105-117.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, pp. 281-297).
Martineau, M., Conte, D., Raveaux, R., Arnault, I., Munier, D., Venturini, G. (2017). A Survey on Image-based Insect Classification. Pattern Recogn., 65(C), 273-284. https://doi.org/10.1016/j.patcog.2016.12.020
Downloads
Published
How to Cite
Issue
Section
License
Copyright © Autors. Creative Commons Attribution 4.0 License. for any article submitted from 6 June 2017 onwards. For manuscripts submitted before, the CC BY 3.0 License was used.
You are free to:
 |
Share — copy and redistribute the material in any medium or format |
 |
Adapt — remix, transform, and build upon the material for any purpose, even commercially. |
Under the following conditions:
 |
Attribution — You must give appropriate credit, provide a link to the licence, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licenser endorses you or your use. |
| No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the licence permits. |
