Sensing Forest for Pattern Recognition

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.
OriginalspracheEnglisch
TitelAdvanced Concepts for Intelligent Vision Systems
Redakteure/-innenJacques Blanc-Talon, Rudi Penne, Wilfried Philips, Dan Popescu, Paul Scheunders
Seitenumfang12
Band10617
ErscheinungsortCham
Herausgeber (Verlag)Springer International Publishing
Erscheinungsdatum23.11.2017
Seiten126-137
ISBN (Print)978-3-319-70352-7
ISBN (elektronisch)978-3-319-70353-4
DOIs
PublikationsstatusVeröffentlicht - 23.11.2017
Veranstaltung18th International Conference, ACIVS 2017 - Antwerp, Belgien
Dauer: 18.09.201721.09.2017

Fingerprint

Untersuchen Sie die Forschungsthemen von „Sensing Forest for Pattern Recognition“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren