Abstract
The Growing Neural Gas (GNG) algorithm is able to perform continuous vector quantization for an unknown distribution while preserving the topological structure of the input space. This makes the GNG attractive for online learning of visual codebooks. However, mapping an input vector to a reference vector is quite expensive and requires an iteration through the entire codebook. We propose a hierarchical extension of the Growing Neural Gas algorithm for online one-shot learning of visual vocabularies. The method intrinsically supports mapping input vectors to codewords in sub-linear time. Further, our extension avoids overfitting and locally keeps track of the topology of the input space. The algorithm is evaluated on both, low dimensional simulated data and high dimensional real world data.
This is a preview of subscription content, log in via an institution to check access.
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
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)
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: A large database for non-parametric object and scene recognition. Pattern Analysis and Machine Intelligence 30(11), 1958–1970 (2008)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV (2003)
Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2006)
Collins, B., Deng, J., Kai, L., Fei-Fei, L.: Towards scalable dataset construction: An active learning approach. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 86–98. Springer, Heidelberg (2008)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)
Schroff, G.F., Criminisi, A., Zisserman, A.: Harvesting image databases from the web. In: ICCV (2007)
Peltason, J., Siepmann, F.H.K., Spexard, T.P., Wrede, B., Hanheide, M., Topp, E.A.: Mixed-initiative in human augmented mapping. In: ICRA (2009)
Fritzke, B.: A growing neural gas network learns topologies. In: NIPS, pp. 625–632 (1995)
Marsland, S., Shapiro, J., Nehmzow, U.: A self-organising network that grows when required. Neural Networks 25(8-9), 1041–1058 (2002)
Burzevski, V., Mohan, C.K.: Hierarchical growing cell structures. In: Neural Networks (1996)
Hodge, V.J., Austin, J.: Hierarchical growing cell structures: TreeGCS. Knowledge and Data Engineering 13(2), 207–218 (2001)
Doherty, K.A.J., Adams, R.G., Davey, N.: Hierarchical growing neural gas. In: Adaptive and Natural Computing Algorithms, pp. 140–143 (2005)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC 2009) Results (2009), http://www.pascal-network.org/challenges/VOC/voc2009/workshop/index.html
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology (2007)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Artificial Neural Networks (1997)
Cao, X., Suganthan, P.N.: Video shot motion characterization based on hierarchical overlapped growing neural gas networks. Multimedia Systems 9(4), 378–385 (2003)
Fritzke, B.: Growing cell structures – a self-organizing network for unsupervised and supervised learning. Neural Networks 7(9), 1441–1460 (1994)
Hamker, F.H.: Life-long learning cell structures – continuously learning without catastrophic interference. Neural Networks 14(4-5), 551–573 (2001)
Campos, M.M., Carpente, G.A.: S-TREE: Self-organizing trees for data clustering and online vector quantization. Neural Networks 14(4), 505–525 (2001)
Furao, S., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Networks 19(1), 90–106 (2006)
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
Kortkamp, M., Wachsmuth, S. (2010). Continuous Visual Codebooks with a Limited Branching Tree Growing Neural Gas. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-15825-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15824-7
Online ISBN: 978-3-642-15825-4
eBook Packages: Computer ScienceComputer Science (R0)