Abstract
This paper presents an unsupervised scene classification method based on the context of features for semantic recognition of indoor scenes used for an autonomous mobile robot. Our method creates Visual Words (VWs) of two types using Scale-Invariant Feature Transform (SIFT) and Gist. Using the combination of VWs, our method creates Bags of VWs (BoVWs) to vote for a two-dimensional histogram as context-based features. Moreover, our method generates labels as a candidate of categories while maintaining stability and plasticity together using the incremental learning function of Adaptive Resonance Theory-2 (ART-2). Our method actualizes unsupervised-learning-based scene classification using generated labels of ART-2 as teaching signals of Counter Propagation Networks (CPNs). The spatial and topological relations among scenes are mapped on the category map of CPNs. The relations of classified scenes that include categories are visualized on the category map. The experiment demonstrates the classification accuracy of semantic categories such as office rooms and corridors using an open dataset as an evaluation platform of position estimation and navigation for an autonomous mobile robot.
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
Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: An experimental and theoretical investigation into simultaneous localization and map building (SLAM). In: Experimental Robotics VI. LNCIS. Springer, Heidelberg (2000)
Wu, J., Rehg, J.M.: CENTRIST: A Visual Descriptor for Scene Categorization. IEEE Trans. Pattern Analysis and Machine Intelligence (2010)
Wu, J., Christensen, H.I., Rehg, J.M.: Visual Place Categorization: Problem, Dataset, and Algorithm. In: Proc. IEEE/RSJ Int’l. Conf. Intelligent Robots and Systems (2009)
Siagain, C., Itti, L.: Rapid Biologically Inspired Scene Classification Using Features Shared with Visual Attention. IEEE Trans. Pattern Analysis and Machine Intelligence 29(2), 300–312 (2007)
Quattoni, A., Torralba, A.: Recognizing Indoor Scenes. In: Proc. Computer Vision and Pattern Recognition (2009)
Tsukada, M., Utsumi, Y., Madokoro, H., Sato, K.: Unsupervised Feature Selection and Category Classification for a Vision-Based Mobile Robot. IEICE Trans. Inf. & Sys. E94-D(1), 127–136 (2011)
Oliva, A., Torralba, A.: Building the gist of a scene: the role of global image features in recognition. In: Visual Perception, Progress in Brain Research, vol. 155 (2006)
Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A.: Context-Based Vision System for Place and Object Recognition. In: Proc. IEEE Int’l. Conf. Computer Vision, pp. 1023–1029 (October 2003)
Yanai, K.: The Current State and Future Directions on Generic Object Recognition. IPSJ SIG Notes CVIM, 121–134 (September 2006)
Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: Proc. IEEE International Conference on Computer Vision, pp. 1150–1157 (1999)
Torralba, A.: How many pixels make an image? Visual Neuroscience 26, 123–131 (2009)
Takeuchi, T.: Underlying Mechanisms of Scene Recognition and Visual Search. ITE Technical Report 33(24), 7–14 (2009)
Nagahashi, T., Ihara, A., Fujiyoshi, H.: Tendency of Image Local Features that are Effective for Discrimination by using Bag-of-Features in Object Category Recognition. IPSJ SIG Notes DVIM (3), 13–20 (2009)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences (1995)
Carpenter, G.A., Grossberg, S.: ART 2: Stable Self-Organization of Pattern Recognition Codes for Analog Input Patterns. Applied Optics 26, 4919–4930 (1987)
Hetch-Nielsen, R.: Counterpropagation networks. In: Proc. of IEEE First Int’l. Conf. on Neural Networks (1987)
Luo, J., Pronobis, A., Caputo, B., Jensfelt, P.: The KTHIDOL2 database. Technical Report CVAP304, Kungliga Tekniska Hoegskolan, CVAP/CAS (October 2006)
Pronobis, A., Xing, L., Caputo, B.: Overview of the CLEF 2009 Robot Vision Track. In: Peters, C., Caputo, B., Gonzalo, J., Jones, G.J.F., Kalpathy-Cramer, J., Müller, H., Tsikrika, T. (eds.) CLEF 2009. LNCS, vol. 6242, pp. 110–119. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Madokoro, H., Utsumi, Y., Sato, K. (2011). Unsupervised Scene Classification Based on Context of Features for a Mobile Robot. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-23851-2_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23850-5
Online ISBN: 978-3-642-23851-2
eBook Packages: Computer ScienceComputer Science (R0)