Abstract
In the paper, we present an improved approach based on Semi-supervised Discriminant Analysis (SDA), called semi-supervised local discriminant embedding (SLDE), for reducing the dimensionality of the feature space. We take the manifold structure into account and try to learn a subspace in which the Euclidean distances can better reflect class structure of the images. The weight matrix and the scatter matrices in SDA are improved to make efficient use of both labeled and unlabeled images. After being embedded into a low-dimensional subspace, the similar images maintain their intrinsic neighbor relations, whereas the dissimilarity neighboring images no longer stick to one another. Experiments have been carried out to validate our approach.
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References
Cai, D., He, X., Han, J.: Semi-supervised Discriminant Analysis. In: Proceedings of the International Conference on Computer Vision (2007)
Wang, F., Zhang, C.S.: Label Propagation through Linear Neighborhoods. Knowledge and Data Engineering 20, 55–67 (2008)
Wang, J.D., Wang, F., Zhang, C.: Linear Neighborhood Propagation and Its Applications. Pattern Analysis and Machine Intelligence 31, 1600–1615 (2009)
He, X.F., Cai, D.: Learning a Maximum Margin Subspace for Image Retrieval. IEEE Trans. Knowledge and data engineering 20, 189–201 (2008)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, Chichester (2001)
Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Trans. CSVT 11, 703–715 (2001)
Pinheiro, A.M.G.: Image Description Using Scale-Space Edge Pixel Directions Histogram. In: Semantic Media Adaptation and Personalization. In: Second International Workshop, pp. 211–218 (2007)
Huijsmans, D.P., Sebe, N.: How to Complete Performance Graphs in Content-Based Image Retrieval: Add Generality and Normalize Scope. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 245–251 (2005)
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Chuan-Bo, H., Zhong, J. (2010). Semi-supervised Local Discriminant Embedding. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_51
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DOI: https://doi.org/10.1007/978-3-642-14922-1_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14921-4
Online ISBN: 978-3-642-14922-1
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