Multi-stream Convolutional Networks for Indoor Scene Recognition

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Anwer, Rao Muhammad
dc.contributor.author Khan, Fahad Shahbaz
dc.contributor.author Laaksonen, Jorma
dc.contributor.author Zaki, Nazar
dc.contributor.editor Vento, Mario
dc.contributor.editor Percannella, Gennaro
dc.date.accessioned 2019-11-07T12:10:15Z
dc.date.available 2019-11-07T12:10:15Z
dc.date.issued 2019-01-01
dc.identifier.citation Anwer , R M , Khan , F S , Laaksonen , J & Zaki , N 2019 , Multi-stream Convolutional Networks for Indoor Scene Recognition . in M Vento & G Percannella (eds) , Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11678 LNCS , Springer Verlag , pp. 196-208 , International Conference on Computer Analysis of Images and Patterns , Salerno , Italy , 03/09/2019 . https://doi.org/10.1007/978-3-030-29888-3_16 en
dc.identifier.isbn 9783030298876
dc.identifier.issn 0302-9743
dc.identifier.issn 1611-3349
dc.identifier.other PURE UUID: fbeff0c1-a173-40ce-b523-dda4ba62c8e7
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/multistream-convolutional-networks-for-indoor-scene-recognition(fbeff0c1-a173-40ce-b523-dda4ba62c8e7).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85072854163&partnerID=8YFLogxK
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/41199
dc.description | openaire: EC/H2020/780069/EU//MeMAD
dc.description.abstract Convolutional neural networks (CNNs) have recently achieved outstanding results for various vision tasks, including indoor scene understanding. The de facto practice employed by state-of-the-art indoor scene recognition approaches is to use RGB pixel values as input to CNN models that are trained on large amounts of labeled data (ImageNet or Places). Here, we investigate CNN architectures by augmenting RGB images with estimated depth and texture information, as multiple streams, for monocular indoor scene recognition. First, we exploit the recent advancements in the field of depth estimation from monocular images and use the estimated depth information to train a CNN model for learning deep depth features. Second, we train a CNN model to exploit the successful Local Binary Patterns (LBP) by using mapped coded images with explicit LBP encoding to capture texture information available in indoor scenes. We further investigate different fusion strategies to combine the learned deep depth and texture streams with the traditional RGB stream. Comprehensive experiments are performed on three indoor scene classification benchmarks: MIT-67, OCIS and SUN-397. The proposed multi-stream network significantly outperforms the standard RGB network by achieving an absolute gain of 9.3%, 4.7%, 7.3% on the MIT-67, OCIS and SUN-397 datasets respectively. en
dc.format.extent 13
dc.format.extent 196-208
dc.language.iso en en
dc.publisher Springer
dc.relation info:eu-repo/grantAgreement/EC/H2020/780069/EU//MeMAD
dc.relation.ispartof International Conference on Computer Analysis of Images and Patterns en
dc.relation.ispartofseries Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings en
dc.relation.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.relation.ispartofseries Volume 11678 LNCS en
dc.rights restrictedAccess en
dc.subject.other Theoretical Computer Science en
dc.subject.other Computer Science(all) en
dc.subject.other 113 Computer and information sciences en
dc.title Multi-stream Convolutional Networks for Indoor Scene Recognition en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Kaski S.
dc.contributor.department Inception Institute of Artificial Intelligence
dc.contributor.department United Arab Emirates University
dc.contributor.department Department of Computer Science en
dc.subject.keyword Depth features
dc.subject.keyword Scene recognition
dc.subject.keyword Texture features
dc.subject.keyword Theoretical Computer Science
dc.subject.keyword Computer Science(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201911076204
dc.identifier.doi 10.1007/978-3-030-29888-3_16


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