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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/3U2JCS5
Repositorysid.inpe.br/sibgrapi/2019/09.09.17.29
Last Update2019:09.09.17.29.19 (UTC) administrator
Metadatasid.inpe.br/sibgrapi/2019/09.09.17.29.19
Metadata Last Update2020:02.19.01.58.22 (UTC) administrator
Citation KeySantosPont:2019:AlLoGl
TitleAlignment of Local and Global Features from Multiple Layers of Convolutional Neural Network for Image Classification
FormatOn-line
Year2019
Access Date2022, Jan. 19
Number of Files1
Size3424 KiB
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Author1 Santos, Fernando Pereira dos
2 Ponti, Moacir Antonelli
Affiliation1 Universidade de São Paulo
2 Universidade de São Paulo
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addressfernando_persan@usp.br
Conference NameConference on Graphics, Patterns and Images, 32 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
DateOct. 28 - 31, 2019
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2019-09-09 17:29:19 :: fernando_persan@usp.br -> administrator ::
2020-02-19 01:58:22 :: administrator -> fernando_persan@usp.br :: 2019
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsfeature learning
convolutional networks
fusion multiple maps
manifold alignment
AbstractConvolutional networks have been extensively applied to obtain features spaces for classification tasks. Although those achieve high accuracy in many scenarios, typically only the top layers of the network are explored. Hence, a relevant question arises from this fact: are initial layers useful in terms of discriminative ability? In this paper, we leverage the complementary description offered by such first layers. Our method consists of features extraction in multiple layers, followed by feature selection, fusion of feature maps from the different layers, and space alignment. Through an extensive experimentation with different datasets and studying different training strategies, our results show that local information, coming from the first layers, may significantly improve the classification performance when merged with a global descriptor extracted from a top layer of the network. We report different methods for reducing the dimensionality of the local descriptors, and guidelines on how to align them so that to perform fusion. Our study encourages future studies on combining feature maps from multiple layers, which may be relevant in particular for transfer learning scenarios.
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