Abstract
We propose in this article an image classification technique based on kernel methods and graphs. Our work explores the possibility of applying marginalized kernels to image processing. In machine learning, performant algorithms have been developed for data organized as real valued arrays; these algorithms are used for various purposes like classification or regression. However, they are inappropriate for direct use on complex data sets. Our work consists of two distinct parts. In the first one we model the images by graphs to be able to represent their structural properties and inherent attributes. In the second one, we use kernel functions to project the graphs in a mathematical space that allows the use of performant classification algorithms. Experiments are performed on medical images acquired with various modalities and concerning different parts of the body.
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References
Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proc. 23rd Int. Conf. on Machine Learning (2006)
Chapelle, O., Haffner, P., Vapnik, V.: Svms for histogram-based image classification. IEEE Transactions on Neural Networks, special issue on Support Vectors (1999)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering object categories in image collections. In: Proc. IEEE Int. Conf. on Computer Vision (ICCV), (2005)
Ros, J., Laurent, C., Jolion, J.M., Simand, I.: Comparing string representations and distances in a natural images classification task. In: GbR 2005, 5th IAPR-TC-15 workshop on graph-based representations, pp. 72–81 ( 2005)
Neuhaus, M., Bunke, H.: Edit distance based kernel functions for attributed graph matching. In: Brun, L., Vento, M. (eds.) GbRPR 2005. LNCS, vol. 3434, pp. 352–361. Springer, Heidelberg (2005)
Neuhaus, M., Bunke, H.: A random walk kernel derived from graph edit distance. In: SSPR/SPR, pp. 191–199 ( 2006)
Vapnik, V.: Statistical Learning Theory. Wiley-Interscience, Chichester (1998)
Gaertner, T., Flach, P., Wrobel, S.: On graph kernels: Hardness results and efficient alternatives. In: Proc. 16th Annual Conf. on Computational Learning Theory, pp. 129–143 ( 2003)
Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized kernels between labeled graphs. In: Proc. 20st Int. Conf. on Machine Learning, pp. 321–328 ( 2003)
Mahé, P., Ueda, N., Akutsu, T., Perret, J.L., Vert, J.P.: Extensions of marginalized graph kernels. In: ICML 2004: Proc. 21st Int. Conf. on Machine Learning (2004)
Borgwardt, K., Vishwanathan, S., Schraudolph, N., Kriegel, H.P.: Protein function prediction via faster graph kernels. In: NIPS Bioinformatics Workshop (2005)
Haris, K., Estradiadis, S.N., Maglaveras, N., Katsaggelos, A.K.: Hybrid image segmentation using watersheds and fast region merging. IEEE Transactions on Image Processing 7, 1684–1699 (1998)
Beaulieu, J.M., Goldberg, M.: Hierarchy in picture segmentation: A stepwise optimization approach. IEEE Trans. Pattern Anal. Mach. Intell. 11, 150–163 (1989)
Brun, L., Mokhtari, M., Meyer, F.: Hierarchical watersheds within the combinatorial pyramid framework. In: Kuba, A., Nyúl, L.G., Palágyi, K. (eds.) DGCI 2006. LNCS, vol. 4245, pp. 34–44. Springer, Heidelberg (2006)
Mahé, P., Ralaivola, L., Stoven, V., Vert, J.P.: The pharmacophore kernel for virtual screening with support vector machines. J. Chem. Inf. Model. 46, 2003–2014 (2006)
Schlkopf, B., Tsuda, K., Vert, J.P.: Kernel Methods in Computational Biology. The MIT Press, Cambridge, Massachussetts (2004)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
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Aldea, E., Atif, J., Bloch, I. (2007). Image Classification Using Marginalized Kernels for Graphs. In: Escolano, F., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2007. Lecture Notes in Computer Science, vol 4538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72903-7_10
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DOI: https://doi.org/10.1007/978-3-540-72903-7_10
Publisher Name: Springer, Berlin, Heidelberg
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