Generalized Operational Classifiers for Material Identification

Xiaoyue Jiang, DIng Wang, Dat Thanh Tran, Serkan Kiranyaz, Moncef Gabbouj, Xiaoyi Feng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Material is one of the intrinsic features of objects, and consequently material recognition plays an important role in image understanding. The same material may have various shapes and appearance, while keeping the same physical characteristic. This brings great challenges for material recognition. Besides suitable features, a powerful classifier also can improve the overall recognition performance. Due to the limitations of classical linear neurons, used in all shallow and deep neural networks, such as CNN, we propose to apply the generalized operational neurons to construct a classifier adaptively. These generalized operational perceptrons (GOP) contain a set of linear and nonlinear neurons, and possess a structure that can be built progressively. This makes GOP classifier more compact and can easily discriminate complex classes. The experiments demonstrate that GOP networks trained on a small portion of the data (4%) can achieve comparable performances to state-of-the-arts models trained on much larger portions of the dataset.

Original languageEnglish
Title of host publicationIEEE 22nd International Workshop on Multimedia Signal Processing, MMSP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728193205
DOIs
StatePublished - 21 Sep 2020
Event22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020 - Virtual, Tampere, Finland
Duration: 21 Sep 202024 Sep 2020

Publication series

NameIEEE 22nd International Workshop on Multimedia Signal Processing, MMSP 2020

Conference

Conference22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020
Country/TerritoryFinland
CityVirtual, Tampere
Period21/09/2024/09/20

Keywords

  • generalized operational perceptrons
  • material identification

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