Sensing Forest for Pattern Recognition


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.
Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems
EditorsJacques Blanc-Talon, Rudi Penne, Wilfried Philips, Dan Popescu, Paul Scheunders
Number of pages12
Place of PublicationCham
PublisherSpringer International Publishing
Publication date23.11.2017
ISBN (Print)978-3-319-70352-7
ISBN (Electronic)978-3-319-70353-4
Publication statusPublished - 23.11.2017
Event18th International Conference, ACIVS 2017 - Antwerp, Belgium
Duration: 18.09.201721.09.2017


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