{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:27:16Z","timestamp":1774880836488,"version":"3.50.1"},"reference-count":60,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"vor","delay-in-days":194,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19A2059"],"award-info":[{"award-number":["U19A2059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802050"],"award-info":[{"award-number":["61802050"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Autonomous object detection powered by cutting\u2010edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine\u2010grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine\u2010grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories\u2010subcategories and subcategories\u2010attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high\u2010level image features. Extensive experiments on the Caltech\u2010UCSD Birds\u2010200\u20102011 dataset verify the superiority of our proposed framework over several existing state\u2010of\u2010the\u2010art methods.<\/jats:p>","DOI":"10.1155\/2021\/8041029","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T18:50:09Z","timestamp":1626288609000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Knowledge Graph Representation Fusion Framework for Fine\u2010Grained Object Recognition in Smart Cities"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8740-876X","authenticated-orcid":false,"given":"Yang","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8475-9203","authenticated-orcid":false,"given":"Ling","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"CaiZ.andHeZ. Trading private range counting over big IoT data Proceedings of the IEEE 39th International Conference on Distributed Computing Systems (ICDCS) July 2019 Dallas TX USA IEEE 144\u2013153 https:\/\/doi.org\/10.1109\/icdcs.2019.00023.","DOI":"10.1109\/ICDCS.2019.00023"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsac.2020.2980802"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.2018.1701245"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.26599\/bdma.2020.9020006"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2019.9010023"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2018.2830307"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2019.2911697"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"\u0160pa\u0148helJ. SochorJ. andMakarovA. Vehicle fine-grained recognition based on convolutional neural networks for real-world applications Proceedings of 2018 14th Symposium on Neural Networks and Applications (NEUREL) November 2018 Belgrade Serbia IEEE 1\u20135.","DOI":"10.1109\/NEUREL.2018.8587012"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2017.2737460"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.085"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"HongQ. ZhangH. NieP. andZhangC. The recognition method of express logistics restricted goods based on deep convolution neural network Proceedings of 2020 5th IEEE International Conference on Big Data Analytics (ICBDA) May 2020 Xiamen China IEEE 363\u2013367 https:\/\/doi.org\/10.1109\/icbda49040.2020.9101222.","DOI":"10.1109\/ICBDA49040.2020.9101222"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1108\/EL-10-2016-0219"},{"key":"e_1_2_9_13_2","volume-title":"Generative Adversarial Networks: A Survey towards Private and Secure Applications","author":"Cai Z.","year":"2021"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.26599\/bdma.2019.9020021"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2020.3007662"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2021.9010026"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2020.010240"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"ZhangN. DonahueJ. GirshickR. andDarrellT. Part-based R-CNNs for fine-grained category detection Proceedings of European Conference on Computer Vision September 2014 Zurich Switzerland Springer 834\u2013849 https:\/\/doi.org\/10.1007\/978-3-319-10590-1_54 2-s2.0-84906514027.","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"HuangS. XuZ. TaoD. andZhangY. Part-stacked CNN for fine-grained visual categorization Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA 1173\u20131182 https:\/\/doi.org\/10.1109\/cvpr.2016.132 2-s2.0-84986333499.","DOI":"10.1109\/CVPR.2016.132"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/mnet.2018.1700349"},{"key":"e_1_2_9_21_2","unstructured":"LiuX. XiaT. WangJ. andLinY. Fully convolutional attention localization networks 2016 arXiv: 1603.06765."},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2774041"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/87"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"XuH. QiG. LiJ. WangM. XuK. andGaoH. Fine-grained Image Classification by Visual-Semantic Embedding Proceedings of IJCAI July 2018 Stockholm Swedan 1043\u20131049.","DOI":"10.24963\/ijcai.2018\/145"},{"key":"e_1_2_9_25_2","unstructured":"Veli\u010dkovi\u0107P. CucurullG. CasanovaA. RomeroA. LioP. andBengioY. Graph attention networks 2017 arXiv:1710.10903."},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"FukuiA. ParkD. H. YangD. RohrbachA. DarrellT. andRohrbachM. Multimodal compact bilinear pooling for visual question answering and visual grounding 2016 arXiv:1606.01847.","DOI":"10.18653\/v1\/D16-1044"},{"key":"e_1_2_9_27_2","article-title":"The Caltech-Ucsd birds-200-2011 dataset","author":"Wah C.","year":"2011","journal-title":"Technical Report CNS-TR-2011-001"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"BransonS. Van HornG. BelongieS. andPeronaP. Bird species categorization using pose normalized deep convolutional nets 2014 arXiv:1406.2952.","DOI":"10.5244\/C.28.87"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2019.9010055"},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"BourdevL. MajiS. andMalikJ. Describing people: a poselet-based approach to attribute classification Proceedings of 2011 International Conference on Computer Vision November 2011 Barcelona Spain IEEE 1543\u20131550 https:\/\/doi.org\/10.1109\/iccv.2011.6126413 2-s2.0-84856647890.","DOI":"10.1109\/ICCV.2011.6126413"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"FarrellR. OzaO. ZhangN. MorariuV. I. DarrellT. andDavisL. S. Birdlets: subordinate categorization using volumetric primitives and pose-normalized appearance Proceedings of 2011 International Conference on Computer Vision November 2011 Barcelona Spain IEEE 161\u2013168.","DOI":"10.1109\/ICCV.2011.6126238"},{"key":"e_1_2_9_32_2","doi-asserted-by":"crossref","unstructured":"ZhangN. FarrellR. andDarrellT. Pose pooling kernels for sub-category recognition Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition June 2012 Providence RI USA IEEE 3665\u20133672 https:\/\/doi.org\/10.1109\/cvpr.2012.6248364 2-s2.0-84866662426.","DOI":"10.1109\/CVPR.2012.6248364"},{"key":"e_1_2_9_33_2","doi-asserted-by":"crossref","unstructured":"LinT. Y. RoyChowdhuryA. andMajiS. Bilinear CNN models for fine-grained visual recognition Proceedings of the IEEE International Conference on Computer Vision December 2015 Santiago Chile 1449\u20131457 https:\/\/doi.org\/10.1109\/iccv.2015.170 2-s2.0-84973863234.","DOI":"10.1109\/ICCV.2015.170"},{"key":"e_1_2_9_34_2","doi-asserted-by":"crossref","unstructured":"GaoY. BeijbomO. ZhangN. andDarrellT. Compact bilinear pooling Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA 317\u2013326 https:\/\/doi.org\/10.1109\/cvpr.2016.41 2-s2.0-84986266770.","DOI":"10.1109\/CVPR.2016.41"},{"key":"e_1_2_9_35_2","doi-asserted-by":"crossref","unstructured":"KongS.andFowlkesC. Low-rank bilinear pooling for fine-grained classification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition July 2017 Honolulu HI USA 365\u2013374 https:\/\/doi.org\/10.1109\/cvpr.2017.743 2-s2.0-85044520090.","DOI":"10.1109\/CVPR.2017.743"},{"key":"e_1_2_9_36_2","doi-asserted-by":"crossref","unstructured":"ZhangH. XuT. ElhoseinyM.et al. SPDA-CNN: unifying semantic part detection and abstraction for fine-grained recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA 1143\u20131152 https:\/\/doi.org\/10.1109\/cvpr.2016.129 2-s2.0-84986309458.","DOI":"10.1109\/CVPR.2016.129"},{"key":"e_1_2_9_37_2","article-title":"Recurrent attentional reinforcement learning for multi-label image recognition","volume":"32","author":"Chen T.","year":"2018","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_2_9_38_2","doi-asserted-by":"crossref","unstructured":"LiuL. WangH. LiG. OuyangW. andLinL. Crowd counting using deep recurrent spatial-aware network 2018 arXiv:1807.00601.","DOI":"10.24963\/ijcai.2018\/118"},{"key":"e_1_2_9_39_2","unstructured":"MnihV. HeessN. GravesA. andKavukcuogluK. Recurrent models of visual attention 2014 arXiv:1406.6247."},{"key":"e_1_2_9_40_2","doi-asserted-by":"crossref","unstructured":"WangZ. ChenT. LiG. XuR. andLinL. Multi-label image recognition by recurrently discovering attentional regions Proceedings of the IEEE International Conference on Computer Vision October 2017 Venice Italy 464\u2013472 https:\/\/doi.org\/10.1109\/iccv.2017.58 2-s2.0-85041927462.","DOI":"10.1109\/ICCV.2017.58"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2019.9010072"},{"key":"e_1_2_9_42_2","doi-asserted-by":"crossref","unstructured":"FuJ. ZhengH. andMeiT. Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition July 2017 Honolulu HI USA 4438\u20134446 https:\/\/doi.org\/10.1109\/cvpr.2017.476 2-s2.0-85041918224.","DOI":"10.1109\/CVPR.2017.476"},{"key":"e_1_2_9_43_2","unstructured":"JaderbergM. SimonyanK. ZissermanA. andKavukcuogluK. Spatial transformer networks 2015 arXiv:1506.02025."},{"key":"e_1_2_9_44_2","doi-asserted-by":"crossref","unstructured":"ZhengH. FuJ. MeiT. andLuoJ. Learning multi-attention convolutional neural network for fine-grained image recognition Proceedings of the IEEE International Conference on Computer Vision July 2017 Honolulu HI USA 5209\u20135217 https:\/\/doi.org\/10.1109\/iccv.2017.557 2-s2.0-85031023277.","DOI":"10.1109\/ICCV.2017.557"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11202"},{"key":"e_1_2_9_46_2","doi-asserted-by":"crossref","unstructured":"HeX.andPengY. Fine-grained image classification via combining vision and language Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition July 2017 Honolulu HI USA 5994\u20136002 https:\/\/doi.org\/10.1109\/cvpr.2017.775 2-s2.0-85042102061.","DOI":"10.1109\/CVPR.2017.775"},{"key":"e_1_2_9_47_2","doi-asserted-by":"crossref","unstructured":"QiX. LiaoR. JiaJ. FidlerS. andUrtasunR. 3d graph neural networks for RGBD semantic segmentation Proceedings of the IEEE International Conference on Computer Vision July 2017 Honolulu HI USA 5199\u20135208 https:\/\/doi.org\/10.1109\/iccv.2017.556 2-s2.0-85041908073.","DOI":"10.1109\/ICCV.2017.556"},{"key":"e_1_2_9_48_2","doi-asserted-by":"crossref","unstructured":"MarinoK. SalakhutdinovR. andGuptaA. The more you know: using knowledge graphs for image classification 2016 arXiv:1612.04844.","DOI":"10.1109\/CVPR.2017.10"},{"key":"e_1_2_9_49_2","doi-asserted-by":"publisher","DOI":"10.3233\/sw-140134"},{"key":"e_1_2_9_50_2","unstructured":"MikolovT. SutskeverI. ChenK. CorradoG. andDeanJ. Distributed representations of words and phrases and their compositionality 2013 arXiv:1310.4546."},{"key":"e_1_2_9_51_2","doi-asserted-by":"crossref","unstructured":"CharikarM. ChenK. andFarach-ColtonM. Finding frequent items in data streams Proceedings of International Colloquium on Automata Languages and Programming July 2002 Malaga Spain Springer 693\u2013703 https:\/\/doi.org\/10.1007\/3-540-45465-9_59.","DOI":"10.1007\/3-540-45465-9_59"},{"key":"e_1_2_9_52_2","doi-asserted-by":"crossref","unstructured":"PhamN.andPaghR. Fast and scalable polynomial kernels via explicit feature maps Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining August 2013 Chicago IL USA 239\u2013247 https:\/\/doi.org\/10.1145\/2487575.2487591 2-s2.0-85023199520.","DOI":"10.1145\/2487575.2487591"},{"key":"e_1_2_9_53_2","doi-asserted-by":"crossref","unstructured":"HanK. GuoJ. ZhangC. andZhuM. Attribute-aware attention model for fine-grained representation learning Proceedings of the 26th ACM International Conference on Multimedia October 2018 Seoul Republic of Korea 2040\u20132048 https:\/\/doi.org\/10.1145\/3240508.3240550 2-s2.0-85058227606.","DOI":"10.1145\/3240508.3240550"},{"key":"e_1_2_9_54_2","doi-asserted-by":"crossref","unstructured":"DubeyA. GuptaO. GuoP. RaskarR. FarrellR. andNaikN. Pairwise confusion for fine-grained visual classification Proceedings of the European Conference on Computer Vision (ECCV) September 2018 Munich Germany 70\u201386 https:\/\/doi.org\/10.1007\/978-3-030-01258-8_5 2-s2.0-85055102549.","DOI":"10.1007\/978-3-030-01258-8_5"},{"key":"e_1_2_9_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2020.3020227"},{"key":"e_1_2_9_56_2","doi-asserted-by":"crossref","unstructured":"ZhengH. FuJ. ZhaZ. J. andLuoJ. Looking for the devil in the details: learning trilinear attention sampling network for fine-grained image recognition Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition June 2019 Long Beach CF USA 5012\u20135021 https:\/\/doi.org\/10.1109\/cvpr.2019.00515.","DOI":"10.1109\/CVPR.2019.00515"},{"key":"e_1_2_9_57_2","doi-asserted-by":"crossref","unstructured":"ChenT. WuW. GaoY. DongL. LuoX. andLinL. Fine-grained representation learning and recognition by exploiting hierarchical semantic embedding Proceedings of the 26th ACM International Conference on Multimedia October 2018 Seoul Republic of Korea 2023\u20132031 https:\/\/doi.org\/10.1145\/3240508.3240523 2-s2.0-85058247246.","DOI":"10.1145\/3240508.3240523"},{"key":"e_1_2_9_58_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6912"},{"key":"e_1_2_9_59_2","unstructured":"LiY. TarlowD. BrockschmidtM. andZemelR. Gated graph sequence neural networks 2015 arXiv:1511.05493."},{"key":"e_1_2_9_60_2","doi-asserted-by":"crossref","unstructured":"AkataZ. ReedS. WalterD. LeeH. andSchieleB. Evaluation of output embeddings for fine-grained image classification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2015 Boston MA USA 2927\u20132936 https:\/\/doi.org\/10.1109\/cvpr.2015.7298911 2-s2.0-84959243017.","DOI":"10.1109\/CVPR.2015.7298911"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/8041029.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/8041029.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/8041029","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T23:17:36Z","timestamp":1723245456000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/8041029"}},"subtitle":[],"editor":[{"given":"Zhihan","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/8041029"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8041029","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-04-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8041029"}}