Abstract
Tree kernels have demonstrated their ability to deal with hierarchical data, as the intrinsic tree structure often plays a discriminative role. While such kernels have been successfully applied to various domains such as nature language processing and bioinformatics, they mostly concentrate on ordered trees and whose nodes are described by symbolic data. Meanwhile, hierarchical representations have gained increasing interest to describe image content. This is particularly true in remote sensing, where such representations allow for revealing different objects of interest at various scales through a tree structure. However, the induced trees are unordered and the nodes are equipped with numerical features. In this paper, we propose a new structured kernel for hierarchical image representations which is built on the concept of subpath kernel. Experimental results on both artificial and remote sensing datasets show that the proposed kernel manages to deal with the hierarchical nature of the data, leading to better classification rates.
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References
Aldea, E., Atif, J., Bloch, I.: Image classification using marginalized kernels for graphs. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 103–113. Springer, Heidelberg (2007)
Blaschke, T., et al.: Geographic object-based image analysis–towards a new paradigm. ISPRS J. of Photogrammetry and Remote Sensing 87, 180–191 (2014)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)
Collins, M., Duffy, N.: Convolution kernels for natural language. In: Advances in Neural Information Processing Systems, pp. 625–632 (2001)
Dupé, F.-X., Brun, L.: Tree covering within a graph kernel framework for shape classification. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 278–287. Springer, Heidelberg (2009)
Harchaoui, Z., Bach, F.: Image classification with segmentation graph kernels. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Haussler, D.: Convolution kernels on discrete structures. Tech. rep., Department of Computer Science, University of California at Santa Cruz (1999)
Kimura, D., Kuboyama, T., Shibuya, T., Kashima, H.: A subpath kernel for rooted unordered trees. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part I. LNCS, vol. 6634, pp. 62–74. Springer, Heidelberg (2011)
Kurtz, C., Passat, N., Gancarski, P., Puissant, A.: Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology. Pattern Recognition 45(2), 685–706 (2012)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Shin, K., Kuboyama, T.: A comprehensive study of tree kernels. In: Nakano, Y., Satoh, K., Bekki, D. (eds.) JSAI-isAI 2013. LNCS, vol. 8417, pp. 337–351. Springer, Heidelberg (2014)
Tilton, J.: RHSEG users manual: Including HSWO, HSEG, HSEGExtract, HSEGReader and HSEGViewer, version 1.50 (2010)
Vishwanathan, S., Smola, A.J.: Fast kernels for string and tree matching. In: Kernel Methods in Computational Biology, pp. 113–130 (2004)
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Cui, Y., Chapel, L., Lefèvre, S. (2015). A Subpath Kernel for Learning Hierarchical Image Representations. In: Liu, CL., Luo, B., Kropatsch, W., Cheng, J. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2015. Lecture Notes in Computer Science(), vol 9069. Springer, Cham. https://doi.org/10.1007/978-3-319-18224-7_4
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DOI: https://doi.org/10.1007/978-3-319-18224-7_4
Publisher Name: Springer, Cham
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