Abstract
Knowledge transfer from related object categories is a key concept to allow learning with few training examples. We present how to use dependent Gaussian processes for transferring knowledge from a related category in a non-parametric Bayesian way. Our method is able to select this category automatically using efficient model selection techniques. We show how to optionally incorporate semantic similarities obtained from the hierarchical lexical database WordNet [1] into the selection process. The framework is applied to image categorization tasks using state-of-the-art image-based kernel functions. A large scale evaluation shows the benefits of our approach compared to independent learning and a SVM based approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: Similarity - measuring the relatedness of concepts. In: Fifth Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 2004), pp. 38–41 (2004)
Ahn, W., Brewer, W.F., Mooney, R.J.: Schema acquisition from a single example. J. of Experim. Psychology: Learning, Memory, and Cognition 18, 391–412 (1992)
Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. PAMI 28(4), 594–611 (2006)
Tommasi, T., Caputo, B.: The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories. In: BMVC (2009)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2005)
Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Gaussian processes for object categorization. IJCV 88(2), 169–188 (2009)
Lawrence, N.D., Platt, J.C., Jordan, M.I.: Extensions of the informative vector machine. In: Deterministic and Statist. Methods in Machine Learn., pp. 56–87 (2004)
Urtasun, R., Quattoni, A., Lawrence, N.D., Darrell, T.: Transfering nonlinear representations using gaussian processes with a shared latent space. In: Proceedings of the Learning Workshop (Snowbird) (2008), MIT-CSAIL-TR-2008-020
Chai, K.M.: Generalization errors and learning curves for regression with multi-task gaussian processes. In: NIPS, pp. 279–287 (2009)
Bonilla, E., Chai, K.M., Williams, C.: Multi-task gaussian process prediction. In: NIPS, pp. 153–160. MIT Press, Cambridge (2008)
Cao, B., Pan, S.J., Zhang, Y., Yeung, D.Y., Yang, Q.: Adaptive transfer learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (2010)
Nickisch, H., Rasmussen, C.E.: Approximations for binary gaussian process classification. Journal of Machine Learning Research 9, 2035–2078 (2008)
Pillonetto, G., Dinuzzo, F., Nicolao, G.D.: Bayesian online multitask learning of gaussian processes. PAMI 2, 193–205 (2010)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. PAMI 29(5), 854–869 (2007)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. PAMI (2010) (in press)
Kiefer, J.: Sequential minimax search for a maximum. Proceedings of the American Mathematical Society 4(3), 502–506 (1953)
Rohrbach, M., Stark, M., Szarvas, G., Schiele, B., Gurevych, I.: What helps where - and why? semantic relatedness for knowledge transfer. In: CVPR (2010)
Marszalek, M., Schmid, C.: Semantic hierarchies for visual object recognition. In: CVPR, pp. 1–7 (2007)
Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: NIPS, pp. 985–992 (2006)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR: Conference on Image and Video Retrieval, pp. 401–408 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rodner, E., Denzler, J. (2010). One-Shot Learning of Object Categories Using Dependent Gaussian Processes. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-15986-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15985-5
Online ISBN: 978-3-642-15986-2
eBook Packages: Computer ScienceComputer Science (R0)