Abstract
The problem of comparing images or image regions can be considered as the problem of matching unordered sets of high dimensional visual features. We show that an hierarchical Growing Neural Gas (GNG) can robustly be used to approximate the optimal partial matching cost between vector sets. Further, we extend the unordered set matching, such that the matching of local features pays attention to the structure of the object and the relative positions of the parts. This view-tuning is also realized with hierarchical GNGs and yields an efficient Mercer Kernel.
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References
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Kortkamp, M., Wachsmuth, S.: Continuous visual codebooks with a limited branching tree growing neural gas. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6354, pp. 188–197. Springer, Heidelberg (2010)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study (2006)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel (2007)
Lu, Z., Ip, H.: Image categorization with spatial mismatch kernels. In: CVPR (2009)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. International Journal of Computer Vision 61(1), 55–79 (2005)
Fergus, R., Perona, P., Zisserman, A.: A sparse object category model for efficient learning and exhaustive recognition. In: CVPR (2005)
Ramanan, D., Baker, S.: Local distance functions: A taxonomy, new algorithms, and an evaluations. In: ICCV (2009)
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistics Quarterly 2(1-2), 83–97 (1955)
Grauman, K., Darrell, T.: Approximate correspondences in high dimensions. In: NIPS (2006)
Odone, F., Barla, A., Verri, A.: Building kernels from binary strings for image matching. IEEE Transactions on Image Processing 14(2), 169–180 (2005)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: ICCV (2003)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: CVPR (2004)
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology (2007)
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Kortkamp, M., Wachsmuth, S. (2011). View-Tuned Approximate Partial Matching Kernel from Hierarchical Growing Neural Gases. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_56
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DOI: https://doi.org/10.1007/978-3-642-21738-8_56
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