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Sensing Forest for Pattern Recognition

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10617))

Abstract

We introduce the Sensing Forest as a novel way of learning how to efficiently sense the visual world for a particular recognition task. The efficiency is evaluated in terms of the resulting recognition performance. We show how the performance depends on the number of sensing values, i.e., the depth of the trees and the size of the forest. We here simulate the sensing process by re-sampling digital images; in future applications one might use dedicated hardware to solve such recognition tasks without acquiring images. We show that our algorithm outperforms traditional Random Forests on the benchmarks MNIST and COIL-100. The basic Sensing Forest is a prototype-based Random Forest with prototypes learned with k-means clustering. Recognition performance can be further increased by using Learning Vector Quantization.

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Correspondence to Irina Burciu .

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Burciu, I., Martinetz, T., Barth, E. (2017). Sensing Forest for Pattern Recognition. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-70353-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70352-7

  • Online ISBN: 978-3-319-70353-4

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