Abstract
Domain adaptation (DA) is a method used to obtain better classification accuracy, when the training and testing datasets have different distributions. This paper describes an algorithm for DA to transform data from source domain to match the distribution of the target domain. We use eigen-analysis of data on both the domains, to estimate the transformation along each dimension separately. In order to parameterize the distributions in both the domains, we perform clustering separately along every dimension, prior to the transformation. The proposed algorithm of DA when applied to the task of object categorization, gives better results than a few state of the art methods.
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
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)
Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: International Conference in Computer Vision, pp. 999–1006 (2011)
Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066–2073 (2012)
Marton, Z.-C., Balint-Benczedi, F., Seidel, F., Goron, L.C., Beetz, M.: Object categorization in clutter using additive features and hashing of part-graph descriptors. In: Stachniss, C., Schill, K., Uttal, D. (eds.) Spatial Cognition 2012. LNCS (LNAI), vol. 7463, pp. 17–33. Springer, Heidelberg (2012)
Jiang, W., Zavesky, E., Fu Chang, S., Loui, A.: Cross-domain learning methods for high-level visual concept classification. In: International Conference on Image Processing, pp. 161–164 (2008)
Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive svms. In: International Conference on Multimedia, pp. 188–197 (2007)
Duan, L., Xu, D., Tsang, I.W.H.: Domain adaptation from multiple sources: A domain-dependent regularization approach. IEEE Transaction in Neural Netwetwork Learning System 23(3), 504–518 (2012)
Qiu, Q., Patel, V.M., Turaga, P., Chellappa, R.: Domain adaptive dictionary learning. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 631–645. Springer, Heidelberg (2012)
Penny, W.: Kl-divergences of normal, gamma, dirichlet and wishart densities. Technical report, Wellcome Department of Cognitive Neurology, University College London (2001)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press (1990)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision Image Understanding 110(3), 346–359 (2008)
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Samanta, S., Das, S. (2013). Domain Adaptation Based on Eigen-Analysis and Clustering, for Object Categorization. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-40261-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
eBook Packages: Computer ScienceComputer Science (R0)