Abstract
A method is presented for the classification of images described using high-dimensional probability density functions (pdfs). A pdf is described by a set of n points sampled from its distribution. These points represent feature vectors calculated from windows sampled from an image. A mapping is found, using the Hungarian algorithm, between the set of points describing a class, and the set for a pdf to be classified, such that the distance that points must be moved to change one set into the other is minimized. The method uses these mappings to create a classifier that can model the variation within each class. The method is applied to the problem of classifying plants based on images of their leaves, and is found to outperform several existing methods.
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References
Backes, A.R., Bruno, O.M.: Plant Leaf Identification Using Multi-scale Fractal Dimension. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 143–150. Springer, Heidelberg (2009)
Cope, J.S., Remagnino, P., Barman, S., Wilkin, P.: Plant Texture Classification Using Gabor Co-occurrences. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammound, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6454, pp. 669–677. Springer, Heidelberg (2010)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV 2004, pp. 1–22 (2004)
Edmonds, J., Karp, R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. Journal of The ACM 19, 248–264 (1972)
Gu, X., Du, J.-X., Wang, X.-F.: Leaf Recognition Based on the Combination of Wavelet Transform and Gaussian Interpolation. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 253–262. Springer, Heidelberg (2005)
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 83–97 (1955)
Parzen, E.: On estimation of a probability density funstion and mode. Annals of Mathematical Statistics 33, 1065–1076 (1962)
Rubner, Y., Guibas, L.J., Tomasi, C.: The earth mover’s distance, multi-dimensional scaling, and color-based image retrieval. In: ARPA Image Understanding Workshop, pp. 661–668 (1997)
Sivic, J., Zisserman, A.: Google video: A text retrieval approach to object matching in videos. In: International Conference on Computer Vision, vol. 2, pp. 1470–1477 (2003)
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Cope, J.S., Remagnino, P. (2012). Utilizing the Hungarian Algorithm for Improved Classification of High-Dimension Probability Density Functions in an Image Recognition Problem. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_24
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DOI: https://doi.org/10.1007/978-3-642-33140-4_24
Publisher Name: Springer, Berlin, Heidelberg
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