Abstract
In order to ignore the limitation of local structure features for the traditional linear dimensionality reduction methods, a new semi-supervised manifold learning is proposed for apple mealiness detection. Assuming the character of the hyperspectral scattering images, an unsupervised non-linear dimensionality reduction method unsupervised discriminant projection (UDP) coupled with sample label information and then develop a semi-supervised learning algorithm, which can keep the local and global structure and can take advantage of the important label information, then get geometric structure optimal linear projection. The classification results with PCA-MUDP are compared with some traditional algorithm. To the two-class classification of ‘mealy’ and ‘non-mealy’ apples, the results show that PCA-MUDP is better than the others.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jimenez, J.J., Segura, R.J.: Collision detection between complex polyhedral. Computers & Graphics 32(4), 402–411 (2008)
Chen, L.F., Liao, H.Y.M., Lin, J.C., Kao, M.D., Yu, G.J.: A New LDA-Based Face Recognition System which Can Solve the Small Sample Size Problem. Pattern Recognition 33(10), 1713–1726 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Tenenbaum, B., Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
He, X., Niyogi, P.: Locality preserving projections. In: Proceedings of the 16th Conference on Neural Information Processing System: NIPS 2003, pp. 585–591. MIT Press, Cambridge (2003)
Yang, J., Zhang, D.: Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans. on Pattern Anal. Machine Intell. 29(4), 650–664 (2007)
Huanga, M., Lu, R.: Apple mealiness detection using hyperspectral scattering technique. Postharvest Biology and Technology 58(3), 168–175 (2010)
Lu, R., Peng, Y.: Hyperspectral Scattering for assessing Peach Fruit Firmness. Biosystems Engineering 93(2), 161–171 (2006)
Huang, M., Zhu, Q.-B.: Feature Extraction of Hyperspectral Scattering Image for Apple Mealiness Based on Singular Value Decomposition. Spectroscopy and Spectral Analysis 31(3), 767–770 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, P., Xu, B., Gao, J. (2012). Modified UDP-Based Semi-supervised Learning for Fruit Internal Quality Detection. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-31020-1_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31019-5
Online ISBN: 978-3-642-31020-1
eBook Packages: Computer ScienceComputer Science (R0)