Abstract
Convolutional Neural Networks (CNN) have proven to be useful tools for object detection and object recognition. They act like feature extractor and classifier at the same time. In this study we present an unsupervised feature selection procedure for constructing a training set for the CNN and analyze in detail the learnt receptive fields. We then introduce, for the first time, a figural alphabet to be used for low-level feature detection with CNN. This alphabet turned out to be useful in detecting a vocabulary set of intermediate level features and considerably reduces the complexity of the CNN. Moreover we propose an optimal high-level feature selection procedure and apply this to the challenging problem of car detection. We demonstrate promising results for multi-class object detection using obtained figural alphabet to detect considerably different categories of objects (e.g., faces and cars).
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References
Blackmore, C., Cooper, G.E.: Development of the Brain Depends on the Visual Environment. Nature 228, 477–478 (1970)
Burl, M., Leung, T., Perona, P.: Face Localization via Shape Statistics. In: Intl. Workshop on Automatic Face and Gesture Recognition (1995)
Fukushima, K.: Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected in Shift Position. Biol. Cybern. 36, 193–202 (1980)
Hubel, D., Wiesel, T.: Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex. Journal of Physiology 160, 106–154 (1962)
Ikeda, H., Kashimura, H., Kato, N., Shimizu, M.: A Novel Autonomous Feature Clustering Model for Image Recognition. In: Proc. of the 8th International Conference on Neural Information Processing (2001)
Kohonen, T.: Self-organizing Maps. Springer, Berlin (1985)
Lawrence, S., Giles, G.L., Tsoi, A.C., Back, A.D.: Face Recognition: A Convolutional Neural Network Approach. IEEE Transactions on Neural Networks 8, 98–113 (1995)
Lecun, Y., Bengio, Y.: Convolutional Networks for Images, Speech, and Time-Series. In: Arbib, M. (ed.) Handbook of Neural Networks and Brain Sciences, pp. 255–258. MIT Press, Cambridge (1995)
Matsugu, M., Mori, K., Ishii, M., Mitarai, Y.: Convolutional Spiking Neural Network Model for Robust Face Detection. In: Proc. of the 9th International Conference on Neural Information Processing, pp. 660–664 (2002)
Papageorgiou, C.P., Oren, M., Poggio, T.: A General Framework of Object Detection. In: Proc. of International Conference on Computer Vision, pp. 555–562 (1998)
Weber, M., Welling, M., Perona, P.: Unsupervised Learning of Models for Recognition. In: Proc. of the 6th European Conference on Computer Vision (2000)
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© 2004 Springer-Verlag Berlin Heidelberg
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Matsugu, M., Cardon, P. (2004). Unsupervised Feature Selection for Multi-class Object Detection Using Convolutional Neural Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_142
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DOI: https://doi.org/10.1007/978-3-540-28647-9_142
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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