Abstract
In this paper we introduce a novel image descriptor, LBP-gist, suitable for real time loop closure detection. As the name suggests, the proposed method builds on two popular image analysis techniques: the gist feature, which has been used in holistic scene description and the LBP operator, originally designed for texture classification. The combination of the two methods gives rise to a very fast computing feature which is shown to be competitive to the state-of-the-art loop closure detection. Fast image search is achieved via Winner Take All Hashing, a simple method for image retrieval that exploits the descriptive power of rank-correlation measures. Two modifications of this method are proposed, to improve its selectivity. The performance of LBP-gist and the hashing strategy is demonstrated on two outdoor datasets.
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
Cummins, M., Newman, P.: FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance. The Int. J. of Rob. Research 27(6), 647–665 (2008)
Angeli, A., Filliat, D., Doncieux, S., Meyer, J.: A Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words. IEEE Transactions on Robotics, Special Issue on Visual Slam 24(5), 1027–1037 (2008)
Gálvez-López, D., Tardos, J.: Bags of binary words for fast place recognition in image sequences. IEEE Transactions on Robotics 28(5), 1188–1197 (2012)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. Journal of Comp. Vision 42(3), 145–175 (2001)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distribution. Pattern Recognition 29(1), 51–59 (1996)
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications of the ACM - 50th Anniversary Issue 51(1), 117–122 (2008)
Yagnik, J., Strelow, D., Ross, D.A., Lin, R.: The power of comparative reasoning. In: International Conference on Computer Vision, pp. 2431–2438 (2011)
Siagian, C., Itti, L.: Biologically Inspired Mobile Robot Vision Localization. IEEE Transactions on Robotics 25(4), 861–873 (2009)
Murillo, A.C., Kosecka, J.: Experiments in place recognition using gist panoramas. In: IEEE Workshop on Omnidirectional Vision, Camera Netwoks and Non-Classical Cameras, ICCV, pp. 2196–2203 (2009)
Ni, K., Kannan, A., Criminisi, A., Winn, J.: Epitomic location recognition. IEEE Trans. on Pattern Analysis and Machine Intell. 31(12), 2158–2167 (2009)
Wu, J., Rehg, J.M.: CENTRIST: A Visual Descriptor for Scene Categorization. IEEE Trans. on Patt. Analysis and Machine Intell. 33(8), 1489–1501 (2011)
Sunderhauf, N., Protzel, P.: BRIEF-Gist - closing the loop by simple means. In: IEEE/RSJ Int. Conference on Intelligent Robots and Systems, pp. 1234–1241 (2011)
Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: IEEE Int. Conference on Computer Vision, pp. 1470–1477 (2003)
Mäenpää, T., Pietikäinen, M.: Multi-scale binary patterns for texture analysis. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 885–892. Springer, Heidelberg (2003)
Topi, M., Timo, O., Matti, P., Maricor, S.: Robust texture classification by subsets of local binary patterns. In: Int. Conf. on Pattern Recognition, vol. 3, pp. 935–938 (2000)
Heikklä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006)
Zhao, G., Kellokumpu, V.-P., Pietikäinen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1301–1306 (2010)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. IEEE Trans. on Patt. Anal. and Machine Intell. 28(18), 2037–2041 (2006)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Twentieth Annual Symposium on Computational Geometry - SCG 2004, pp. 253–262 (2004)
Blanco, J.-L., Moreno, F.-A., Gonzalez, J.: A collection of outdoor robotic datasets with centimeter-accuracy ground truth. Auton. Robots 27(4), 327–351 (2009)
Paulevé, L., Jégou, H., Amsaleg, L.: Locality sensitive hashing: A comparison of hash function types and querying mechanisms. Pattern Recognition Letters 31(11), 1348–1358 (2010)
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
Campos, F.M., Correia, L., Calado, J.M.F. (2013). Loop Closure Detection with a Holistic Image Feature. In: Correia, L., Reis, L.P., Cascalho, J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science(), vol 8154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40669-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-40669-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40668-3
Online ISBN: 978-3-642-40669-0
eBook Packages: Computer ScienceComputer Science (R0)